Integrating Deep Learning and Radiomics in Differentiating Papillary Thyroid Microcarcinoma from Papillary Thyroid Carcinoma with Ultrasound Images
Bibliographic record
Abstract
Bing Yu,1,2,* Huijuan He,3,* Qiao Zheng,2,* Yao Ai,2 Xianwen Yu,2 Sunjun Li,4 Ji Zhang,2 Juebin Jin,5 Xiance Jin,2,6 Wenliang Yu3 1Purchasing Center Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, People’s Republic of China; 2Radiation and Medical Oncology Department, Wenzhou Medical University First Affiliated Hospital, Wenzhou, 325000, People’s Republic of China; 3Radiation Oncology Department, Quzhou People’s Hospital, Quzhou, 324002, People’s Republic of China; 4Alberta College, Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 5Medical Engineering Department, Wenzhou Medical University First Affiliated Hospital, Wenzhou, 325000, People’s Republic of China; 6School of Basic Medical Science, Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiance Jin, Radiation and Medical Oncology Department, Wenzhou Medical University First Affiliated Hospital, Wenzhou, 325000, People’s Republic of China, Email jinxc1979@hotmail.com Wenliang Yu, Radiation Oncology Department, Quzhou People’s Hospital, Quzhou, 324002, People’s Republic of China, Email wlyu1982@aliyun.comPurpose: The feasibility and accuracy of ultrasound-based radiomics, deep learning, and combined deep learning radiomics models were investigated in the differentiation of papillary thyroid carcinoma and papillary thyroid microcarcinoma to decrease the risk of overtreatment of papillary thyroid microcarcinoma.Methods: A total of 549 patients with confirmed 180 papillary thyroid carcinoma and 436 papillary thyroid microcarcinoma nodules from Hospital One were enrolled and randomly divided into training and validation cohorts at a ratio of 8:2 with 56 patients left as independent testing set 1. Fifty patients from Hospital Two were enrolled as independent testing set 2. Radiomics signature and five deep learning networks, such as visual geometry group 13 (VGG13), VGG16, VGG19, AlexNet, and EfficientNet, were generated for papillary thyroid carcinoma and papillary thyroid microcarcinoma differentiation. Combined deep learning and radiomics models were constructed to further improve the differentiation ability.Results: An area under curves of 0.826 and 0.822 was achieved with radiomics model for papillary thyroid carcinoma and papillary thyroid microcarcinoma differentiation in the independent testing set 1 and set 2, respectively. VGG19 achieved the best area under curves of 0.890 and EfficientNet achieved the best accuracy of 0.867. The best accuracy and area under curves of 0.904, 0.900, and 0.931, 0.946 were achieved with the combination of VGG + radiomics (R_V_Combined) and EffiecientNet + radiomics (R_E_Combined) in the independent testing set 1 and set 2, respectively.Conclusion: Deep learning and radiomics combination models are promising in the noninvasively preoperative differentiation of papillary thyroid microcarcinoma and papillary thyroid carcinoma to decrease the overtreatment of patients with papillary thyroid microcarcinoma and to minimize the complications caused by overtreatment.Keywords: papillary thyroid carcinoma, papillary thyroid microcarcinoma, ultrasound, deep learning, radiomics
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".