Prediction of 131I uptake in lung metastases of differentiated thyroid cancer using deep learning.
An accurate assessment of 131I accumulation capacity in lung metastases of differentiated thyroid cancer (DTC) is pivotal for guiding radioiodine therapy and avoiding ineffective 131I administration. This study aimed to develop a deep convolutional neural network (DCNN) model to predict 131I uptake in lung metastases of DTC before radioiodine therapy.
In this retrospective, multicenter, population-based cohort study, we collected chest CT image datasets for DTC patients with lung metastases from three hospitals in China. Pulmonary metastases were classified into two categories based on the post therapeutic 131I whole-body scan: 131I-avid (positive 131I uptake) and non-131I-avid (negative 131I uptake). For DCNN model development, patients were assigned to the primary dataset (140 patients with 131I-avid, 121 with non-131I-avid). For model validation, patients were assigned to the internal validation dataset (36 patients with 131I-avid, 23 with non-131I-avid), external validation dataset 1 (25 patients with 131I-avid, 18 with non-131I-avid), and external validation dataset 2 (23 patients with 131I-avid, 18 with non-131I-avid). Using these datasets, we assessed the performance of our model, ResNeSt50, and compared it with two models: Inception V3 and ResNet50.
Compared to Inception V3 and ResNet50, our model, ResNeSt50, demonstrated the highest prediction performance in the internal (area under the curve [AUC] = 0.722, 95% confidence interval [CI] = 0.716-0.725), external validation dataset 1 (AUC = 0.720, 95% CI = 0.691-0.749), and external validation dataset 2 (AUC = 0.731, 95% CI = 0.713-0.748).
We developed a simple and robust DCNN model for predicting the 131I uptake in lung metastases of DTC before radioiodine therapy, which can provide improved screening for patients who may benefit from 131I therapy.
Chinese Clinical Trial Registry (ChiCTR), ChiCTR1800018047. Registered on 28 August 2018.
In this retrospective, multicenter, population-based cohort study, we collected chest CT image datasets for DTC patients with lung metastases from three hospitals in China. Pulmonary metastases were classified into two categories based on the post therapeutic 131I whole-body scan: 131I-avid (positive 131I uptake) and non-131I-avid (negative 131I uptake). For DCNN model development, patients were assigned to the primary dataset (140 patients with 131I-avid, 121 with non-131I-avid). For model validation, patients were assigned to the internal validation dataset (36 patients with 131I-avid, 23 with non-131I-avid), external validation dataset 1 (25 patients with 131I-avid, 18 with non-131I-avid), and external validation dataset 2 (23 patients with 131I-avid, 18 with non-131I-avid). Using these datasets, we assessed the performance of our model, ResNeSt50, and compared it with two models: Inception V3 and ResNet50.
Compared to Inception V3 and ResNet50, our model, ResNeSt50, demonstrated the highest prediction performance in the internal (area under the curve [AUC] = 0.722, 95% confidence interval [CI] = 0.716-0.725), external validation dataset 1 (AUC = 0.720, 95% CI = 0.691-0.749), and external validation dataset 2 (AUC = 0.731, 95% CI = 0.713-0.748).
We developed a simple and robust DCNN model for predicting the 131I uptake in lung metastases of DTC before radioiodine therapy, which can provide improved screening for patients who may benefit from 131I therapy.
Chinese Clinical Trial Registry (ChiCTR), ChiCTR1800018047. Registered on 28 August 2018.
Authors
Song Song, Fei Fei, Tao Tao, Qiu Qiu, Shen Shen, Chen Chen, Luo Luo, She She, Wang Wang, Zhang Zhang, Luo Luo
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