Artificial Intelligence Applications in Urology

Chen, Andrew B.; Haque, Taseen F.; Roberts, Sidney; Rambhatla, Sirisha; Cacciamani, Giovanni; Hung, Andrew J.

doi:10.1016/j.ucl.2021.07.009

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…Our findings highlight that an artificial intelligence (AI) model is required in the pre-operative planning of advanced testicular tumors compared to the traditional pre-planning by conventional imaging. Currently, AI models and machine learning models are gaining popularity in the field of urology [ 22 , 23 , 24 ]; our results represent the application of an AI model and the utility of handcrafted radiomics in uro-oncology.…”

Section: Discussionmentioning

confidence: 71%

CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons

et al. 2023

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Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016–2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher’s exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1–0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55–0.87) for A, and 0.275 (IC 0.11–0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher’s exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.

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