Abstract
Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).
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Data Availability Statement
The relevant thyroid spectrum dataset has been deposited in figshare (https://figshare.com). The project DOI is https://doi.org/10.6084/m9.figshare.21579546.
Abbreviations
- PTC:
-
Papillary thyroid carcinoma
- NIR:
-
Near infrared
- HE:
-
Hematoxylin-eosin
- SVM:
-
Support vector machines
- CARS:
-
Competitive adaptive reweighted sampling
- FD:
-
First derivative
- SD:
-
Second derivative
- SG:
-
Savitzky–Golay
- MC:
-
Mean centering
- SNV:
-
Standard normal variate
- MSC:
-
Multiplicative scatter correction
- PLS:
-
Partial least squares
- RMSECV:
-
Root mean square error of cross validation
- T3:
-
Triiodothyronine
- T4:
-
Tetraiodothyronine
- FT3:
-
Free triiodothyronine
- FT4:
-
Free thyroxine
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Funding
This work was supported by the grant awarded by the National Natural Science Foundation of China (No. 62225112; No. 61831015), and the key research and development project of Anhui Province (No. 202104j07020059).
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BLZ contributed to literature searching and wrote the manuscript. BLZ, Y Wang, and MHH designed the experiments and interpreted the results of the manuscript. BLZ, Y Wang, MHH, Y Wu, JNL, QLL, MD, WQS, and GTZ supervised the study and revised the manuscript. All authors read and approved the final manuscript.
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Zhao, B., Wang, Y., Hu, M. et al. Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype. Phenomics 3, 469–484 (2023). https://doi.org/10.1007/s43657-023-00113-1
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DOI: https://doi.org/10.1007/s43657-023-00113-1