Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.

PURPOSE: A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study. METHODS: A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis. RESULTS: The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis. CONCLUSION: This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Breast cancer pre-clinical screening using infrared thermography and artificial intelligence: a prospective, multicentre, diagnostic accuracy cohort study.

BACKGROUND: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening. MATERIALS AND METHODS: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital. RESULTS: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals. CONCLUSIONS: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.