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.