Artificial intelligence-based model for lymph node metastases detection in bladder cancer - Authors' reply.

Abstract

We appreciate the Correspondences from Zhipeng Mai and colleagues and Kang Zou and colleagues on our retrospective, multicentre study developing an artificial intelligence-based model for the detection of lymph node metastases on whole slide images in bladder cancer. 1 Wu S Hong G Xu A et al. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study. Lancet Oncol. 2023; 24: 360-370 Summary Full Text Full Text PDF PubMed Scopus (2) Google Scholar Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic studyWe developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. Full-Text PDF Artificial intelligence-based model for lymph node metastases detection in bladder cancerArtificial intelligence can reach the level of trained pathologists in terms of diagnostic accuracy, thereby substantially reducing the clinical workload of front-line pathologists in the future.1 However, artificial intelligence-based models require large-scale validation in real-world clinical scenarios.1,2 In their Article,3 Shaoxu Wu and colleagues conducted a large-scale, multicentre, retrospective study on the use of an artificial intelligence-based model for the diagnosis of lymph node metastases in bladder cancer. Full-Text PDF Artifical intelligence-based model for lymph node metastases detection in bladder cancerWe read with interest the Article by Shaoxu Wu and colleagues,1 which reports on the development of a diagnostic model based on pathomics for the detection of lymph node metastases in bladder cancer. Although the authors stated that their artificial intelligence-based model could achieve 100% sensitivity with an acceptable false positive rate, lowering the diagnostic threshold to achieve high sensitivity is bound to sacrifice the corresponding positive predictive value (PPV). Among the five validation sets, the PPVs of three sets were less than satisfactory (ie, <0·700), especially in medical centres with a low proportion of patients with lymph node metastases-positive images, such as the Third Affiliated Hospital of Sun Yat-sen University, which reported a PPV of only 0·283. Full-Text PDF