Next Article in Journal
Impact of Pretreatment Ischemic Location on Functional Outcome after Thrombectomy
Next Article in Special Issue
Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning
Previous Article in Journal
Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
Previous Article in Special Issue
Ophthalmic Ultrasonography in Sub-Saharan Africa—A Kinshasa Experience
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 11
Issue 11
10.3390/diagnostics11112033
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal

by
Vincenza Granata
1,
Giovanni Morana
2,
Mirko D'Onofrio
3,
Roberta Fusco
4,*,
Francesca Coppola
5,
Francesca Grassi
6,7,
Salvatore Cappabianca
7,
Alfonso Reginelli
7,
Nicola Maggialetti
8,
Duccio Buccicardi
9,
Antonio Barile
6,10,
Marco Rengo
11,
Chandra Bortolotto
12,
Fabrizio Urraro
7,
Giorgia Viola La Casella
7,
Marco Montella
7,
Eleonora Ciaghi
13,
Francesco Bellifemine
13,
Federica De Muzio
14,
Ginevra Danti
15,
Giulia Grazzini
15,
Carmelo Barresi
16,
Luca Brunese
14,
Emanuele Neri
6,17,
Roberto Grassi
6,7,
Vittorio Miele
6,15 and
Lorenzo Faggioni
17add Show full author list remove Hide full author list
1
Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
2
Department of Radiology, Santa Maria di Ca’ Foncello Hospital, 31100 Treviso, Italy
3
Department of Radiology, University Hospital G.B. Rossi, University of Verona, 37134 Verona, Italy
4
Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
5
Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
6
Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
7
Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80125 Naples, Italy
8
Section of Radiodiagnostic, DSMBNOS, Aldo Moro University, 70124 Bari, Italy
9
Imaging Department, San Paolo Hospital, 17100 Savona, Italy
10
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
11
Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
12
Department of Radiology, I.R.C.C.S. Policlinico San Matteo Foundation, 27100 Pavia, Italy
13
Exprivia SpA, 70056 Bari, Italy
14
Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Via Francesco De Sanctis 1, 86100 Campobasso, Italy
15
Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy
16
Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, Siena University Hospital, 53100 Siena, Italy
17
Department of Translational Research, University of Pisa, 56126 Pisa, Italy
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
Diagnostics 2021, 11(11), 2033; https://doi.org/10.3390/diagnostics11112033
Submission received: 12 October 2021 / Revised: 31 October 2021 / Accepted: 1 November 2021 / Published: 3 November 2021
(This article belongs to the Special Issue Advances in Diagnostic Medical Imaging)
Versions Notes

Abstract

:
Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.

1. Introduction

Pancreatic cancer accounts for almost as many deaths (466,000) as cases (496,000) because of its poor prognosis and is the seventh leading cause of cancer death in both sexes. Rates are from 4-fold to 5-fold higher in higher Human Development Index (HDI) countries, with the highest incidence rates in Europe, Northern America, and Australia/New Zealand [1]. Both incidence and mortality rates either have been stable or have slightly increased in many countries, likely reflecting the increasing prevalence of obesity, diabetes, and alcohol consumption, although improvements in diagnostic and cancer registration practices may also be in play in some countries [1,2,3,4]. Given that the rates of this disease are rather stable relative to the declining rates of breast cancer, it has been projected that pancreatic cancer will surpass breast cancer as the third leading cause of cancer death by 2025 in a study of 28 European countries [1].
Pancreatic ductal adenocarcinoma (PDAC) is a challenge for a multidisciplinary oncology team. Although many patients have locally advanced disease at diagnosis, the only curative treatment is surgery, and systemic chemotherapy is usually the key therapy [5,6,7,8,9]. The multidisciplinary team should make the choice concerning the resectability of pancreatic cancer following the acquisition of a complete staging [10,11]. Computed tomography (CT) has grown to be the tool of choice in the preoperative diagnosis guiding treatment planning, as well as during follow-up [12,13]. Several researchers considered magnetic resonance imaging (MRI) to be equivalent to CT in detecting and staging. However, recent evidence recommends the addition of MRI as a diagnostic integration to identify lesions undetected by CT as well as the presence of liver metastases [14,15]. MRI is a useful diagnostic tool in oncologic patients since this offers morphological data by T2-weighted (W) and T1-W sequences, and functional data by diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE)-MRI, as well as new tools such as blood oxygenation-level dependent (BOLD) sequences [14,15].
Imaging evaluation plays a central and primary role in the initial decision making process of patients with PDAC. There are, however, limitations in the current reporting of these imaging studies. These include variability of the descriptive terminology that attempts to define disease extent and incomplete documentation of disease sites which may affect prognosis and adversely affect treatment planning by surgeons, and medical and radiation oncologists [16]. An effective communication of imaging data to referring physicians is crucial for patient care. Radiology reports are traditionally created as free-text reports (FRT) in narrative language. However, inconsistencies with regard to content, style, and presentation can hamper data transfer and reduce the clarity of the reports, which can in turn negatively affect the extraction of the required key information by the referring physician [16,17,18,19,20,21,22]. Therefore, FRT should be shifted toward structured reports (SRs). The use of templates in SR provides a checklist as to whether all relevant items for a specific procedure are addressed. Moreover, thanks to this “structure”, SR allows the correlation of radiological data with other key clinical features, guiding to a precise diagnosis and personalized medicine [23,24,25,26,27,28].
Despite the evident improvements, SRs have not yet become approved in the radiological routine. The main reasons are the current lack of usable templates and the minimal availability of software solutions [19]. Therefore, the Italian Society of Medical and Interventional Radiology (SIRM) elaborated an Italian warehouse of SR templates that can be freely accessed by all SIRM members, with the purpose of the routine use in a clinical setting [29].
For PDCA patients, the aim of treatment should be curative when possible. The tumor size, location within the pancreas, local extent which may involve surrounding vessels, and the presence of metastatic lesions should be assessed before planning treatment. All these features should be reported in a radiological template, and therefore an adequate communication between the radiologist and multidisciplinary group is required. To improve this communication and meet the needs of clinicians, the aim of this study is to propose an SR template for PDAC based on CT and MRI study in the systematic reporting of neoplasm findings at the staging phase.

2. Materials and Methods

2.1. Panel Expert

A multi-round consensus-building Delphi exercise, and subsequent extensive discussion between expert radiologists, was completed to create comprehensive focused SR templates for CT and MRI during the staging phase of patients with PDAC.
A SIRM radiologist, an expert in abdominal imaging, created the first drafts of the SRs. A working team of 20 experts was set up, with members from the Italian College of Gastro-enteric Radiologists and of Diagnostic Imaging in Oncology Radiologists from SIRM. Their aim was to revise the initial drafts iteratively, with the objective of reaching a final consensus on SRs.

2.2. Selection of the Delphi Domains and Items

All the experts reviewed literature data on the main scientific databases, including PubMed, Scopus, and Google Scholar, to assess papers on pancreatic cancer imaging and structured radiology reports from December 2000 to August 2021. All panelists reviewed the full text of the selected studies, and each of them shared the list of Delphi items via emails and/or teleconferences.
The CT-SR was divided into four sections: (a) Patient Clinical Data, (b) Clinical Evaluation, (c) Imaging Protocol, and (d) Report. A dedicated section of significant images was added as part of the report.
The MRI-SR was divided into four sections: (a) Patient Clinical Data, (b) Clinical Evaluation, (c) Imaging Protocol, and (d) Report. A dedicated section of significant images was added as part of the report.
Two Delphi rounds were performed. During the first round, each panelist independently contributed to refining the SR drafts by means of online meetings or email exchanges. The level of panelists’ agreement for each SR models was tested in the second Delphi through a Google Form questionnaire shared by email. Each expert expressed individual comments for each specific template section using a five-point Likert scale (1 = strongly disagree, 2 = slightly disagree, 3 = slightly agree, 4 = generally agree, 5 = strongly agree).
After the second Delphi round, the last version of the SRs was generated on the dedicated RSNA website (radreport.org) using a T-Rex template format, in line with IHE (Integrating the Healthcare Enterprise) and the MRRT (Management of Radiology Report Templates) profiles, accessible as open-source software, with the technical support of Exprivia (Exprivia SpA, Bari, Italy). These determine both the format of radiology report templates (using version 5 of HyperText Markup Language (HTML5)) and the transporting mechanism to request, retrieve, and stock these schedules [30]. The radiology report was structured using a series of “codified queries” integrated in the T-Rex editor’s preselected sections [30].

2.3. Statistical Analysis

Answers from each panelist were exported in Microsoft Excel® format for ease of data collection and statistical analysis.
All ratings of panelists for each section were analyzed with descriptive statistics measuring the mean score, the standard deviation, and the sum of scores. A mean score of 3 was considered good and a score of 4 excellent.
To measure the internal consistency of the panelist ratings for each section of the report, a quality analysis based on the average inter-item correlation was performed with Cronbach’s alpha (Cα) correlation coefficient [31,32]. An alpha coefficient (α) ≥ 0.9 was considered excellent, α ≥ 0.8 good, α ≥ 0.7 acceptable, α ≥ 0.6 questionable, α ≥ 0.5 poor, and α < 0.5 unacceptable. However, in the iterations an α of 0.8 was considered a reasonable goal for internal reliability.
The data analysis was performed using the Statistic Toolbox of MATLAB (The MathWorks, Inc., Natick, MA, USA).

3. Results

3.1. Structured Report

The final CT-SR (Supplementary Materials) version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR.
The final MRI-SR (Supplementary Materials) version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR.
For both templates, only the report section must be compiled, and all other sections are optional.
The “Patient Clinical Data” section included patient anthropometric data, previous or family history of malignancies, including pancreatic cancer, risk factors, including genetic mutations, and predisposing diseases. In this section, we included the item “Allergies” to drug or no drug and contrast medium.
The “Clinical Evaluation” section collected previous examination results, a genetic panel, results of histopathological examination on biopsy specimen, carbohydrate antigen 19.9 (Ca 19.9) level, carcinoembryonic antigen (CEA) level, blood count, serum creatinine, liver function, and clinical symptoms.
The “Imaging Protocol” section for CT-SR included data on the equipment used, including data on the reconstruction algorithm and slice thickness. In addition, we collected data on contrast study protocol, including data on the contrast study phase, as well as data concerning the contrast medium, and ongoing adverse events.
The “Imaging Protocol” section for MRI-SR included data on the scanner brand and model, protocol details as conventional or abbreviated, and sequences. In addition, we collected data on contrast study protocol, including data on the contrast study phase, as well as data concerning the contrast medium.
The “Report” section included data on:
1. Lesion: tumor visible or not visible and indirect signs; size; structure; site; vascularity.
2. Arteries: normal or variant anatomy; atherosclerotic; presence of vessel involvement; distance between celiac trunk and infiltrated hepatic artery >5 mm.
3. Veins: normal or variant anatomy; thrombosis (neoplastic or not neoplastic); presence of vein involvement; for superior mesenteric vein, longitudinal extent of infiltration >20 mm and tumor involvement of first jejunal loop.
In addition, in this section, we included data on biliary ducts, posterior lamina, loco-regional diffusion (including stomach, spleen, treits, liver, etc.), node stage, and for MRI-SR, liver metastases; for CT-SR, metastases stage (including liver, bone, lung, etc.), peritoneal carcinomatosis, as well as the presence of incidental radiological findings, including acute pancreatitis and pulmonary embolism.

3.2. Consensus Agreement

In the first round, as reported in Table 1, for CT-SR, all sections by 20 panelists received more than a good rating. The overall mean score of the experts was 4.85 (range 2–5). The Cα correlation coefficient was 0.85 for the CT staging structured report and the sum of scores was 1844 (92.20 ± 5.57).
Table 2 reports single score and sum of scores of 20 panelists for the MR staging structured report in the first round. In the first round, as reported in Table 2, all sections received more than a good rating. The overall mean score of the experts was 4.73 (range 1–5). The Cα correlation coefficient was 0.82 and the sum of scores for the MR structured report was 1798 (89.90 ± 6.64).
Table 3 reports single score and sum of scores of 20 panelists for the CT staging structured report in the second round: all sections received more than a good rating. The overall mean score of the experts was 4.87 (range 3–5), the Cα correlation coefficient was 0.94, and the sum of scores was 1850 (92.50 ± 4.03).
In the second round for the MR structured report, as reported in Table 4, all sections by 20 panelists received more than a good rating. The overall mean score of the experts was 4.91 (range 3–5), the Cα correlation coefficient was 0.93, and the sum of scores for the MR structured report was 1108 (93.35 ± 3.28).
For both the CT and MR pancreas structured report, between the first and second round, a major agreement was reached among the 20 panelists highlighted by the increase of Cα correlation coefficient, overall mean score, and sum of scores.

4. Discussion

The advantages of SR are centered around 11 major themes: accuracy/quality, retrievability, accessibility, automatization, facilitation of workflows, keeping the electronic patient record (EPR) up to date, teleradiology, information exchange between medical centers, ergonomics of the radiologist and the referring physician, financial benefits, and education [25,26]. Accuracy and quality assurance of the report were important topics. SR would encourage radiologists to employ a specific lexicon and therefore keep them from hiding behind vague and verbose reports [26]. A tiered approach to SR has been described [26]. At its basic level, SR should be organized with headings, such as clinical history, indication, technique, findings, and impression. The next tier is where the “findings” section is organized with subheadings, such as the various organs imaged. At the highest tier, the SR has all of the previously mentioned features and uses a standardized language based on a universally accepted lexicon [26]. The present SRs are based on standardized terminology and structures, features required in order to adhere to diagnostic–therapeutic recommendations and enrolment in clinical trials, reducing any ambiguity that may arise from non-conventional language and enable better communication between radiologists and clinicians so that they are third-level reports [26].
According to our knowledge, a similar work was already carried out by a multi-institutional group of 15 experts that included radiologists, hepatopancreatobiliary surgeons, and gastroenterologists, composed of members of the Society of Abdominal Radiology and the American Pancreatic Association, who convened a consensus conference during the annual American Pancreatic Association meeting (Chicago, November 2011) [16]. A draft template including the most appropriate findings chosen from the available templates based on the state of knowledge and available pertinent literature was developed by consensus during the meeting. A final draft was prepared by the lead author and sent to all participants for review, comments, and approval. Unlike the template presented in 2014 [16], the present templates were based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging. In addition, in this project we promoted two templates, one for CT and one for MRI, which, unlike what is promoted by Al-Hawary [16], are composed of a mandatory section which is “the report section” and three optional sections. In fact, the final CT-SR version was built by 52 items, including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. The final MRI-SR version was built by 49 items, including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. For both CT and MR pancreas SR, between the first and second round, a major agreement was reached among the 20 panelists highlighted by the increase of Cα correlation coefficient, overall mean score, and sum of scores.
We think that this result is due to the awareness of the need to identify the essential features to be reported in a radiological report and, from another point of view, from the idea that today there is a need to integrate clinical data with radiological data. Although the present template may seem long and difficult and therefore slow down the workflow of a radiologist, it is necessary to emphasize that only the report section is mandatory, while the others are optional. Furthermore, considering that not all data may be available to the radiologist, they are open fields that can also be filled in a later time. In addition to this, the possibility of connecting this template with the patient’s electronic file allows for an automatic import of the available data. Beyond the idea of the scrupulousness of these reports, we believe it is useful to also fill in the optional fields; in fact, the “Patient Clinical Data” section included patient anthropometric data, previous or family history of malignancies, risk factors and predisposing diseases, family history of pancreatic cancer, hereditary syndromes, and other genetic mutations. The “Clinical Evaluation” section collected previous examination results, a genetic panel, results of histopathological examination on biopsy specimen, carbohydrate antigen 19.9 (Ca 19.9) level, carcinoembryonic antigen (CEA) level, blood count, serum creatinine, liver function, and clinical symptoms. These data could create the basis of a large database, allowing not only for the carrying out of epidemiological statistical analysis, but they could be used to build a radiomics model by combining radiological features and clinical data [33,34,35,36,37,38,39]. In this context, the added value of genomic data could be used to develop a model of radiogenomics, which was helpful regarding the highest level of personalized risk stratification and the advanced precision medicine process [40,41,42,43,44,45].
With regard to the “Imaging Protocol” section, revealing the examination technique, not only within one’s own department, but also with radiology departments of other centers, rejoins to a double reason: the standardization and the optimization of the study protocols. For example, during oncological follow-up, different acquisition parameters such as different segmentation algorithms are crucial elements that can lead to variability in volumetric assessment. Thus, for CT, slice thickness and other protocol-related factors, and for MRI in diffusion sequences, and b-values, should persist as unvariable for reliable measurements to be performed. In the step of protocol optimization, enhanced communication between the different centers can theoretically lead to quality improvement through enhanced patient safety (e.g., radiation dose reduction), contrast optimization, and image quality [46].
Regarding to the “Report” section, this included data on (a) lesion, as if tumor is detectable or not and the presence of indirect signs: pancreatic atrophy, displaced calcifications in patient with chronic calcific pancreatitis, duct-to-parenchyma ratio greater than 0.34, double duct sign, vessel encasement, vessel deformity, superior mesenteric artery (SMA) to superior mesenteric vein (SMV) ratio greater than 1; the lesion size and site such as the lesion structure and vascularity. On (b) arteries, as normal or variant anatomy; atherosclerotic; presence of involvement (less than or equal to 180° tumor contact of the vessel circumference is described as “abutment” and more than 180° tumor contact of the vessel circumference is referred to as “encasement”); distance between celiac trunk and infiltrated hepatic artery >5 mm. On (c) veins, as normal or variant anatomy; thrombosis (neoplastic or not neoplastic); presence of involvement (less than or equal to 180° tumor contact of the vessel circumference is described as “abutment” and more than 180° tumor contact of the vessel circumference is referred to as “encasement”); for superior mesenteric vein, longitudinal extent of infiltration >20 mm and tumor involvement of first jejunal loop.
The presence and degree of contact between the tumor and the peripancreatic vessels is of paramount importance in determining surgical resectability. According to Lu et al. [47], the vascular involvement by the pancreatic tumor is based on the percentage of circumferential surface contact between the tumor and the adjacent vessel, and that more than 180° of tumor–vessel contact is highly specific for tumor unresectability. In addition, irregularity of the vessel contour (including “tear drop” deformity) or change in caliber is also considered a sign of vascular invasion regardless of the degree of contact between tumor and vessel [48]. Furthermore, several additional imaging findings that are pertinent for surgical planning are the presence of tumor or bland venous thrombosis; the extension of tumor contact with the common hepatic artery (CHA) to the level of the origins of right and left hepatic arteries; the extension of tumor contact to first superior mesenteric artery (SMA) branch and to most proximal draining vein into SMV and the arterial variants, and in particular, origin of the right hepatic artery from the SMA.
In addition, in this section, we included data on biliary ducts, posterior lamina, loco-regional diffusion (including stomach, spleen, treits, Liver, etc.), node stage and for MRI-SR, liver metastases; for CT-SR, metastases stage (including liver, bone, lung, etc.), peritoneal carcinomatosis, as well as the presence of incidental radiological findings, including acute pancreatitis and pulmonary embolism for CT-SR.
The opportunity and benefit of templates to lead the radiologist’s workflow allows the reporting of all essential radiological findings that might be ignored in an FRT through simple distraction. For example, the radiologist should report the arterial variant, especially the presence of a replaced hepatic artery or hepatomesenteric trunk, since the presence of anatomic arterial variants increases the risk for intraoperative vascular injuries and postoperative complications such as hepatic ischemia, biliary anastomotic leak, and pseudoaneurysms. This information may not be provided if the radiological investigation is not performed in a dedicated center and therefore using a checklist and a systematic search pattern may help to prevent such diagnostic errors. In addition, SRs have been shown to enhance clinical impact on tumor staging and surgical planning for pancreatic cancer [49,50]. Brook et al. compared the results of SR versus FRT of CT findings for the staging and assessment of resectability in PDAC patients, showing that surgeons were more confident about tumor resectability using SR [49].
Extensive proposal of SR is an essential goal in order to offer referring physicians and patients the best quality of service, and for researchers the best quality data in the context of big data development. Implementation is complex, requiring mature technology, organizational and interoperability challenges, especially adequate storage of data, and easy and adequate connections with PACS- and post-processing software. Consequently, introduction of SR should be seen as a comprehensive effort, affecting all domains of radiology [51,52,53,54,55,56,57,58,59,60,61,62].
Despite the promising results obtained, this study has some limitations. First, the panelists were only radiologists; therefore, a multidisciplinary approach is lacking. A multidisciplinary validation of SR would be appropriate. Second, the panelists were of the same nationality; the contribution of experts from multiple countries would allow for broader sharing and would increase the consistency of the SR. Finally, this study was not aimed at assessing the impact of the SR on the clinical setting.

5. Conclusions

The present templates, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, promoted the use of SR for CT and MRI evaluation in PDCA patients. For both CT and MR pancreas SR, between the first and second round, a major agreement was reached among the 20 panelists highlighted by the increase of Cα correlation coefficient, overall mean score, and sum of scores. This result is due to the awareness of the need to identify the essential features to be reported in a radiological report and, from another point of view, from the idea that today there is a need to integrate clinical and radiological data.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/diagnostics11112033/s1.

Author Contributions

Conceptualization, V.G. and R.G.; Data curation, V.G.; Investigation, V.G., G.M., R.F., F.C., F.G., S.C., A.R., N.M., D.B., A.B., M.R., C.B. (Chandra Bortolotto), F.U., G.V.L.C., M.M., E.C., G.G., C.B. (Carmelo Barresi), L.B., E.N., R.G., V.M. and L.F.; Methodology, V.G., G.M., M.D., F.B., F.D.M. and G.D.; Writing—original draft, V.G.; Writing—review & editing, V.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are reported in the manuscript.

Conflicts of Interest

The authors have no conflict of interest to be disclosed. The authors confirm that the article is not under consideration for publication elsewhere. Each author has participated sufficiently to take public responsibility for the content of the manuscript.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
  2. Izzo, F.; Granata, V.; Fusco, R.; D’Alessio, V.; Petrillo, A.; Lastoria, S.; Piccirillo, M.; Albino, V.; Belli, A.; Nasti, G.; et al. A multicenter randomized controlled prospective study to assess efficacy of laparoscopic electrochemotherapy in the treatment of locally advanced pancreatic Cancer. J. Clin. Med. 2021, 10, 4011. [Google Scholar] [CrossRef]
  3. Granata, V.; Grassi, R.; Fusco, R.; Belli, A.; Palaia, R.; Carrafiello, G.; Miele, V.; Petrillo, A.; Izzo, F. Local ablation of pancreatic tumors: State of the art and future perspectives. World J. Gastroenterol. 2021, 27, 3413–3428. [Google Scholar] [CrossRef]
  4. Granata, V.; Grassi, R.; Fusco, R.; Galdiero, R.; Setola, S.V.; Palaia, R.; Belli, A.; Silvestro, L.; Cozzi, D.; Brunese, L.; et al. Pancreatic cancer detection and characterization: State of the art and radiomics. Eur. Rev. Med. Pharmacol. Sci. 2021, 25, 3684–3699. [Google Scholar] [CrossRef] [PubMed]
  5. AlMasri, S.S.; Zenati, M.S.; Desilva, A.; Nassour, I.; Boone, B.A.; Singhi, A.D.; Bartlett, D.L.; Liotta, L.A.; Espina, V.; Loughran, P.; et al. Encouraging long-term survival following autophagy inhibition using neoadjuvant hydroxychloroquine and gemcitabine for high-risk patients with resectable pancreatic carcinoma. Cancer Med. 2021, 10, 7233–7241, Epub ahead of print. [Google Scholar] [CrossRef] [PubMed]
  6. Izzo, F.; Granata, V.; Fusco, R.; D’Alessio, V.; Petrillo, A.; Lastoria, S.; Piccirillo, M.; Albino, V.; Belli, A.; Tafuto, S.; et al. Clinical phase I/II study: Local disease control and survival in locally advanced pancreatic cancer treated with electrochemotherapy. J. Clin. Med. 2021, 10, 1305. [Google Scholar] [CrossRef] [PubMed]
  7. Iyengar, S.; Nevala-Plagemann, C.; Garrido-Laguna, I. Updates on adjuvant and neoadjuvant treatment strategies for surgically resectable and borderline resectable pancreatic ductal adenocarcinoma. Ther. Adv. Med. Oncol. 2021, 13. [Google Scholar] [CrossRef]
  8. Granata, V.; Grassi, R.; Fusco, R.; Setola, S.V.; Palaia, R.; Belli, A.; Miele, V.; Brunese, L.; Petrillo, A.; Izzo, F. Assessment of ablation therapy in pancreatic cancer: The radiologist’s challenge. Front. Oncol. 2020, 10, 2666. [Google Scholar] [CrossRef] [PubMed]
  9. Higashi, M.; Tanabe, M.; Okada, M.; Furukawa, M.; Iida, E.; Ito, K. Influence of fat deposition on T1 mapping of the pancreas: Evaluation by dual-flip-angle MR imaging with and without fat suppression. Radiol. Med. 2020, 125, 1–6. [Google Scholar] [CrossRef]
  10. Yang, H.K.; Park, M.-S.; Choi, M.; Shin, J.; Lee, S.S.; Jeong, W.K.; Hwang, S.H.; Choi, S.H. Systematic review and meta-analysis of diagnostic performance of CT imaging for assessing resectability of pancreatic ductal adenocarcinoma after neoadjuvant therapy: Importance of CT criteria. Abdom. Radiol. 2021, 1–17. [Google Scholar] [CrossRef]
  11. Cen, C.; Liu, L.; Li, X.; Wu, A.; Liu, H.; Wang, X.; Wu, H.; Wang, C.; Han, P.; Wang, S. Pancreatic ductal adenocarcinoma at CT: A combined nomogram model to preoperatively predict cancer stage and survival outcome. Front. Oncol. 2021, 11, 1980. [Google Scholar] [CrossRef]
  12. Sun, J.; Yang, L.; Zhou, Z.; Zhang, D.; Han, W.; Zhang, Q.; Peng, Y. Performance evaluation of two iterative reconstruction algorithms, MBIR and ASIR, in low radiation dose and low contrast dose abdominal CT in children. Radiol. Med. 2020, 125, 918–925. [Google Scholar] [CrossRef]
  13. Granata, V.; Fusco, R.; Catalano, O.; Setola, S.V.; Castelguidone, E.D.L.D.; Piccirillo, M.; Palaia, R.; Grassi, R.; Granata, F.; Izzo, F.; et al. Multidetector computer tomography in the pancreatic adenocarcinoma assessment: An update. Infect. Agents Cancer 2016, 11, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Granata, V.; Fusco, R.; Sansone, M.; Grassi, R.; Maio, F.; Palaia, R.; Tatangelo, F.; Botti, G.; Grimm, R.; Curley, S.; et al. Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging. Ther. Adv. Gastroenterol. 2020, 13, 1756284819885052. [Google Scholar] [CrossRef]
  15. McKinney, M.; Griffin, M.O.; Tolat, P.P. Multimodality imaging for the staging of pancreatic cancer. Surg. Oncol. Clin. N. Am. 2021, 30, 621–637. [Google Scholar] [CrossRef] [PubMed]
  16. Al-Hawary, M.M.; Francis, I.R.; Chari, S.T.; Fishman, E.; Hough, D.M.; Lu, D.S.; Macari, M.; Megibow, A.; Miller, F.H.; Mortele, K.J.; et al. Pancreatic ductal adenocarcinoma radiology reporting template: Consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association. Radiology 2014, 270, 248–260. [Google Scholar] [CrossRef] [Green Version]
  17. European Society of Radiology (ESR). ESR paper on structured reporting in radiology. Insights Imaging 2018, 9, 1–7. [Google Scholar] [CrossRef] [Green Version]
  18. Granata, V.; Caruso, D.; Grassi, R.; Cappabianca, S.; Reginelli, A.; Rizzati, R.; Masselli, G.; Golfieri, R.; Rengo, M.; Regge, D.; et al. Structured reporting of rectal cancer staging and restaging: A consensus proposal. Cancers 2021, 13, 2135. [Google Scholar] [CrossRef] [PubMed]
  19. Faggioni, L.; Coppola, F.; Ferrari, R.; Neri, E.; Regge, D. Usage of structured reporting in radiological practice: Results from an Italian online survey. Eur. Radiol. 2017, 27, 1934–1943. [Google Scholar] [CrossRef]
  20. Neri, E.; Coppola, F.; Larici, A.R.; Sverzellati, N.; Mazzei, M.A.; Sacco, P.; Dalpiaz, G.; Feragalli, B.; Miele, V.; Grassi, R. Structured reporting of chest CT in COVID-19 pneumonia: A consensus proposal. Insights Imaging 2020, 11, 1–9. [Google Scholar] [CrossRef]
  21. Bosmans, J.M.; Weyler, J.J.; Parizel, P.M. Structure and content of radiology reports, a quantitative and qualitative study in eight medical centers. Eur. J. Radiol. 2009, 72, 354–358. [Google Scholar] [CrossRef] [PubMed]
  22. Heikkinen, K.; Loyttyniemi, M.; Kormano, M. Structure and content of 400 CT reports in four teaching hospitals using a new, Windows-based software tool. Acta Radiol. 2000, 41, 102–105. [Google Scholar] [CrossRef]
  23. Naik, S.S.; Hanbidge, A.; Wilson, S.R. Radiology reports: Examining radiologist and clinician preferences regarding style and content. AJR Am. J. Roentgenol. 2001, 176, 591–598. [Google Scholar] [CrossRef] [PubMed]
  24. Wallis, A.; McCoubrie, P. The radiology report—Are we getting the message across? Clin. Radiol. 2011, 66, 1015–1022. [Google Scholar] [CrossRef]
  25. Bosmans, J.M.; Peremans, L.; Menni, M.; De Schepper, A.M.; Duyck, P.O.; Parizel, P.M. Structured reporting: If, why, when, how-and at what expense? Results of a focus group meeting of radiology professionals from eight countries. Insights Imaging 2012, 3, 295–302. [Google Scholar] [CrossRef] [Green Version]
  26. Weiss, D.L.; Langlotz, C.P. Structured reporting: Patient care enhancement or productivity nightmare? Radiology 2008, 249, 739–747. [Google Scholar] [CrossRef] [PubMed]
  27. Granata, V.; Pradella, S.; Cozzi, D.; Fusco, R.; Faggioni, L.; Coppola, F.; Grassi, R.; Maggialetti, N.; Buccicardi, D.; Lacasella, G.V.; et al. Computed tomography structured reporting in the staging of lymphoma: A Delphi consensus proposal. J. Clin. Med. 2021, 10, 4007. [Google Scholar] [CrossRef]
  28. Fiorini, F.; Granata, A.; Battaglia, Y.; Karaboue, M.A.A. Talking about medicine through mass media. G. Ital. Nefrol. Organo Uff. Soc. Ital. Nefrol. 2019, 36, 2019-vol1. [Google Scholar]
  29. Available online: https://sirm.org (accessed on 15 September 2021).
  30. Kahn, C.E.; Genereaux, B.; Langlotz, C.P. Conversion of radiology reporting templates to the MRRT standard. J. Digit. Imaging 2015, 28, 528–536, PMCID:PMC4570901. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Becker, G. Creating comparability among reliability coefficients: The case of Cronbach Alpha and Cohen Kappa. Psychol. Rep. 2000, 87, 1171. [Google Scholar] [CrossRef]
  32. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef] [Green Version]
  33. Danti, G.; Berti, V.; Abenavoli, E.; Briganti, V.; Linguanti, F.; Mungai, F.; Pradella, S.; Miele, V. Diagnostic imaging of typical lung carcinoids: Relationship between MDCT, 111In-Octreoscan and 18F-FDG-PET imaging features with Ki-67 index. Radiol. Med. 2020, 125, 715–729. [Google Scholar] [CrossRef]
  34. Farchione, A.; Larici, A.R.; Masciocchi, C.; Cicchetti, G.; Congedo, M.T.; Franchi, P.; Gatta, R.; Cicero, S.L.; Valentini, V.; Bonomo, L.; et al. Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data. Radiol. Med. 2020, 125, 625–635, Epub 2020 Mar 3. [Google Scholar] [CrossRef] [PubMed]
  35. Zhang, G.; Yang, Z.; Gong, L.; Jiang, S.; Wang, L.; Zhang, H. Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations. Radiol. Med. 2020, 125, 374–383. [Google Scholar] [CrossRef] [PubMed]
  36. Granata, V.; Fusco, R.; Costa, M.; Picone, C.; Cozzi, D.; Moroni, C.; La Casella, G.; Montanino, A.; Monti, R.; Mazzoni, F.; et al. Preliminary report on computed tomography radiomics features as biomarkers to immunotherapy selection in lung adenocarcinoma patients. Cancers 2021, 13, 3992. [Google Scholar] [CrossRef]
  37. Granata, V.; Fusco, R.; Barretta, M.L.; Picone, C.; Avallone, A.; Belli, A.; Patrone, R.; Ferrante, M.; Cozzi, D.; Grassi, R.; et al. Radiomics in hepatic metastasis by colorectal cancer. Infect. Agents Cancer 2021, 16, 1–9. [Google Scholar] [CrossRef] [PubMed]
  38. Kirienko, M.; Ninatti, G.; Cozzi, L.; Voulaz, E.; Gennaro, N.; Barajon, I.; Ricci, F.; Carlo-Stella, C.; Zucali, P.; Sollini, M.; et al. Computed Tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Radiol. Med. 2020, 125, 951–960. [Google Scholar] [CrossRef] [PubMed]
  39. Hu, H.-T.; Shan, Q.-Y.; Chen, S.-L.; Li, B.; Feng, S.-T.; Xu, E.-J.; Li, X.; Long, J.-Y.; Xie, X.-Y.; Lu, M.-D.; et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: Technical reproducibility of acquisition and scanners. Radiol. Med. 2020, 125, 697–705. [Google Scholar] [CrossRef]
  40. Nazari, M.; Shiri, I.; Hajianfar, G.; Oveisi, N.; Abdollahi, H.; Deevband, M.R.; Oveisi, M.; Zaidi, H. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning. Radiol. Med. 2020, 125, 754–762. [Google Scholar] [CrossRef] [Green Version]
  41. Fusco, R.; Granata, V.; Mazzei, M.A.; Di Meglio, N.; Del Roscio, D.; Moroni, C.; Monti, R.; Cappabianca, C.; Picone, C.; Neri, E.; et al. Quantitative imaging decision support (QIDSTM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control 2021, 28, 1073274820985786. [Google Scholar] [CrossRef]
  42. Granata, V.; Fusco, R.; Avallone, A.; De Stefano, A.; Ottaiano, A.; Sbordone, C.; Brunese, L.; Izzo, F.; Petrillo, A. Radiomics-derived data by contrast enhanced magnetic resonance in RAS mutations detection in colorectal liver metastases. Cancers 2021, 13, 453. [Google Scholar] [CrossRef]
  43. Granata, V.; Fusco, R.; Risi, C.; Ottaiano, A.; Avallone, A.; De Stefano, A.; Grimm, R.; Grassi, R.; Brunese, L.; Izzo, F.; et al. Diffusion-weighted MRI and diffusion kurtosis imaging to detect RAS mutation in colorectal liver metastasis. Cancers 2020, 12, 2420. [Google Scholar] [CrossRef] [PubMed]
  44. Smedley, N.F.; Aberle, D.R.; Hsu, W. Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer. J. Med. Imaging (Bellingham) 2021, 8, 031906. [Google Scholar] [CrossRef]
  45. Moreno, S.; Bonfante, M.; Zurek, E.; Cherezov, D.; Goldgof, D.; Hall, L.; Schabath, M. A radiogenomics ensemble to predict EGFR and KRAS mutations in NSCLC. Tomography 2021, 7, 154–168. [Google Scholar] [CrossRef]
  46. Reiner, B.I. Strategies for radiology reporting and communication: Part 4: Quality assurance and education. J. Digit. Imaging 2014, 27, 1–6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Lu, D.S.; Reber, H.A.; Krasny, R.M.; Kadell, B.M.; Sayre, J. Local staging of pancreatic cancer: Criteria for unresectability of major vessels as revealed by pancreatic-phase, thin- section helical CT. AJR Am. J. Roentgenol. 1997, 168, 1439–1443. [Google Scholar] [CrossRef]
  48. Wong, J.C.; Lu, D.S. Staging of pancreatic adenocarcinoma by imaging studies. Clin. Gastroenterol. Hepatol. 2008, 6, 1301–1308. [Google Scholar] [CrossRef] [PubMed]
  49. Brook, O.R.; Brook, A.; Vollmer, C.M.; Kent, T.S.; Sanchez, N.; Pedrosa, I. Structured reporting of multiphasic CT for pancreatic cancer: Potential effect on staging and surgical planning. Radiology 2015, 274, 464–472. [Google Scholar] [CrossRef]
  50. Marcal, L.P.; Fox, P.S.; Evans, D.B.; Fleming, J.B.; Varadhachary, G.R.; Katz, M.H.; Tamm, E.P. Analysis of free-form radiology dictations for completeness and clarity for pancreatic cancer staging. Abdom. Imaging 2015, 40, 2391–2397. [Google Scholar] [CrossRef]
  51. Grassi, R.; Miele, V.; Giovagnoni, A. Artificial intelligence: A challenge for third millennium radiologist. Radiol. Med. 2019, 124, 241–242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Van Assen, M.; Muscogiuri, G.; Caruso, D.; Lee, S.J.; Laghi, A.; De Cecco, C.N. Artificial intelligence in cardiac radiology. Radiol. Med. 2020, 125, 1186–1199. [Google Scholar] [CrossRef]
  53. Neri, E.; Coppola, F.; Miele, V.; Bibbolino, C.; Grassi, R. Artificial intelligence: Who is responsible for the diagnosis? Radiol. Med. 2020, 125, 517–521. [Google Scholar] [CrossRef] [Green Version]
  54. Carlos, R.C.; Kahn, C.; Halabi, S. Data science: Big data, machine learning, and artificial intelligence. J. Am. Coll. Radiol. 2018, 15, 497–498, Erratum in J. Am. Coll. Radiol. 2018, 15, 819. [Google Scholar] [CrossRef]
  55. Morris, M.A.; Saboury, B.; Burkett, B.; Gao, J.; Siegel, E.L. Reinventing radiology: Big data and the future of medical imaging. J. Thorac. Imaging. 2018, 33, 4–16. [Google Scholar] [CrossRef]
  56. Aphinyanaphongs, Y. Big data analyses in health and opportunities for research in radiology. Semin. Musculoskelet. Radiol. 2017, 21, 32–36. [Google Scholar] [CrossRef]
  57. Kansagra, A.P.; Yu, J.-P.J.; Chatterjee, A.R.; Lenchik, L.; Chow, D.S.; Prater, A.B.; Yeh, J.; Doshi, A.M.; Hawkins, C.M.; Heilbrun, M.E.; et al. Big data and the future of radiology informatics. Acad. Radiol. 2016, 23, 30–42. [Google Scholar] [CrossRef]
  58. Bauckneht, M.; Albano, D.; Annunziata, S.; Santo, G.; Guglielmo, P.; Frantellizzi, V.; Branca, A.; Ferrari, C.; Vento, A.; Mirabile, A.; et al. Somatostatin receptor PET/CT imaging for the detection and staging of pancreatic NET: A systematic review and meta-analysis. Diagnostics 2020, 10, 598. [Google Scholar] [CrossRef]
  59. Arnone, A.; Laudicella, R.; Caobelli, F.; Guglielmo, P.; Spallino, M.; Abenavoli, E.; Martini, A.L.; Filice, R.; Comis, A.D.; Cuzzocrea, M.; et al. Clinical impact of 18F-FDG PET/CT in the diagnostic workup of pancreatic ductal adenocarcinoma: A systematic review. Diagnostics 2020, 10, 1042. [Google Scholar] [CrossRef]
  60. Chang, C.; Sun, X.; Zhao, W.; Wang, R.; Qian, X.; Lei, B.; Wang, L.; Liu, L.; Ruan, M.; Xie, W.; et al. Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (</= 3 cm): PET/CT findings and correlations with lymph node metastasis. Radiol. Med. 2020, 125, 257–264. [Google Scholar] [CrossRef]
  61. Avesani, G.; Arshad, M.; Lu, H.; Fotopoulou, C.; Cannone, F.; Melotti, R.; Aboagye, E.; Rockall, A. Radiological assessment of Peritoneal Cancer Index on preoperative CT in ovarian cancer is related to surgical outcome and survival. Radiol. Med. 2020, 125, 770–776. [Google Scholar] [CrossRef]
  62. Granata, V.; Grassi, R.; Miele, V.; Larici, A.R.; Sverzellati, N.; Cappabianca, S.; Brunese, L.; Maggialetti, N.; Borghesi, A.; Fusco, R.; et al. Structured Reporting of lung cancer staging: A consensus proposal. Diagnostics 2021, 11, 1569. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Single score and sum of scores of panelists for CT staging structured report (I round).
Table 1. Single score and sum of scores of panelists for CT staging structured report (I round).
Panelist NumberA1. Anthropometric DataA2. Personal AssessmentsA3. Allergies and Adverse ReactionsB1. Clinical InformationC1. Exam DataC2. Use of Contrast Agent and Study ProtocolC3. Adverse EventsD1. Primary LesionD2. ArteryD3. VeinD4. Biliary TractD5. Posterior FoilD6. Loco-regional DiffusionD7. Locoregional LymphadenopathiesD8. Distant MetastasisD9. Acute PancreatitisD10. Pulmonary EmbolismD11. Accessory FindsD12. ConclusionsSum
1555555555555555555595
2555555555555555555595
3445555555555554555592
4445555555555554555592
5555555555555555555595
6555555555555555555595
7545555455555555555593
8545555555555555555594
9444444444444444444476
10555555555555555555595
11443555455545555445587
12555555555555555555595
13555555555555555555595
14555555555555555555595
15555555555555555555595
16542324453444555545578
17555555555555555555595
18445555555555555554592
19555555555555555555595
20555555555555555555595
Mean4.754.604.704.854.804.904.804.954.854.904.854.904.954.954.854.904.854.904.9592.20
Std0.440.500.800.490.700.310.410.220.490.310.370.310.220.220.370.310.370.310.225.57
Table
Table 2. Single score and sum of scores of panelists for MR staging structured report (I round).
Table 2. Single score and sum of scores of panelists for MR staging structured report (I round).
Panelist NumberA1. Anthropometric DataA2. Personal AssessmentsA3. Allergies and Adverse ReactionsB1. Clinical InformationC1. Exam DataC2. Study ProtocolC3. Contrast AgentC4. Adverse EventsD1. Primary LesionD2. ArteryD3. VeinD4. Biliary TractD5. Posterior FoilD6. Loco-regional DiffusionD7. Locoregional LymphadenopathiesD8. Distant MetastasisD9. Acute PancreatitisD11. Accessory FindsD12. ConclusionsSum
1545555555555555555594
2555555555555555555595
3444455555545555555489
4445555555555555555593
5445555555555555555593
6444444444444444444476
7444555555555555554591
8422332344554555545575
9545555555555555555594
10545555545555555555593
11535531555555555555587
12545555555555555555594
13444444444444444444476
14555555555555555555595
15433455545555555544586
16555555555555555555595
17445555555555555555593
18445555555555555555593
19555555555555555555595
20444555555555555554591
Mean4.454.004.504.704.704.554.804.754.854.904.854.854.904.904.904.904.804.754.8589.90
Std0.510.730.830.570.661.100.520.440.370.310.370.370.310.310.310.310.410.440.376.64
Table
Table 3. Single score and sum of scores of panelists for CT staging structured report (II round).
Table 3. Single score and sum of scores of panelists for CT staging structured report (II round).
Panelist NumberA1. Anthropometric DataA2. Personal AssessmentsA3. Allergies and Adverse ReactionsB1. Clinical InformationC1. Exam DataC2. Use of Contrast Agent and Study ProtocolC3. Adverse EventsD1. Primary LesionD2. ArteryD3. VeinD4. Biliary TractD5. Posterior FoilD6. Loco-regional DiffusionD7. Locoregional LymphadenopathiesD8. Distant MetastasisD9. Acute PancreatitisD10. Pulmonary EmbolismD11. Accessory FindsD12. ConclusionsSum
1555555555555555555595
2555555555555555555595
3443555355545555444585
4555555555555555555595
5555555555555555555595
6555555555555555345592
7545354534445555455585
8555555555555555555595
9445555555555555554592
10555555555555555555595
11555555555555555555595
12555555555555555555595
13555555555555555555595
14555555555555555555595
15443555355545555444585
16555555555555555555595
17555555555555555555595
18555545555555555345591
19545354534445555455585
20555555555555555555595
Mean4.854.754.804.804.954.904.804.804.904.904.805.005.005.005.004.604.804.855.0092.50
Std0.370.440.620.620.220.310.620.620.310.310.410.000.000.000.000.680.410.370.004.03
Table
Table 4. Single score and sum of scores of panelists for MR staging structured report (II round).
Table 4. Single score and sum of scores of panelists for MR staging structured report (II round).
Panelist NumberA1. Anthropometric DataA2. Personal AssessmentsA3. Allergies and Adverse reactionsB1. Clinical InformationC1. Exam DataC2. Use of Contrast Agent and Study ProtocolC3. Adverse EventsD1. Primary LesionD2. ArteryD3. VeinD4. Biliary TractD5. Posterior FoilD6. Loco-regional DiffusionD7. Locoregional LymphadenopathiesD8. Distant MetastasisD9. Acute PancreatitisD10. Pulmonary EmbolismD11. Accessory FindsD12. ConclusionsSum
1555555555555555555595
2555555555555555555595
3533555535554554544585
4555555555555555555595
5555555555555555555595
6555555555555555555595
7545554455544555555590
8555555555555555555595
9445555555555555555593
10555555555555555555595
11555555555555555555595
12555555555555555555595
13555555555555555555595
14555555555555555555595
15333555535555555545586
16555555555555555555595
17555555555555555555595
18555555555555555555595
19555354553455555545588
20555555555555555555595
Mean4.854.704.804.905.004.904.954.804.904.954.954.905.005.004.955.004.854.955.0093.35
Std0.490.660.620.450.000.310.220.620.450.220.220.310.000.000.220.000.370.220.003.28
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Granata, V.; Morana, G.; D'Onofrio, M.; Fusco, R.; Coppola, F.; Grassi, F.; Cappabianca, S.; Reginelli, A.; Maggialetti, N.; Buccicardi, D.; et al. Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal. Diagnostics 2021, 11, 2033. https://doi.org/10.3390/diagnostics11112033

AMA Style

Granata V, Morana G, D'Onofrio M, Fusco R, Coppola F, Grassi F, Cappabianca S, Reginelli A, Maggialetti N, Buccicardi D, et al. Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal. Diagnostics. 2021; 11(11):2033. https://doi.org/10.3390/diagnostics11112033

Chicago/Turabian Style

Granata, Vincenza, Giovanni Morana, Mirko D'Onofrio, Roberta Fusco, Francesca Coppola, Francesca Grassi, Salvatore Cappabianca, Alfonso Reginelli, Nicola Maggialetti, Duccio Buccicardi, and et al. 2021. "Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal" Diagnostics 11, no. 11: 2033. https://doi.org/10.3390/diagnostics11112033

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop