Volumetric assessment of unaffected parenchyma and Wilms' tumours: analysis of response to chemotherapy and surgery using a semi‐automated segmentation algorithm in children with renal neoplasms. (27th February 2020) - Rickard, Mandy; Fernandez, Nicolas; Blais, Anne‐Sophie; Shalabi, Ahmed; Amirabadi, Afsaneh; Traubici, Jeffrey; Lee, Wayne; Gleason, Joseph; Brzezinski, Jack; Lorenzo, Armando J.
Abstract : Objective: To present our proof of concept with semi‐automatic image recognition/segmentation technology for calculation of tumour/parenchyma volume. Methods: We reviewed Wilms' tumours (WTs) between 2000 and 2018, capturing computed tomography images at baseline, after neoadjuvant chemotherapy (NaC) and postoperatively. Images were uploaded into MATLAB‐3‐D volumetric image processing software. The program was trained by two clinicians who supervised the demarcation of tumour and parenchyma, followed by automatic recognition and delineation of tumour margins on serial imaging, and differentiation from uninvolved renal parenchyma. Volume was automatically calculated for both. Results: During the study period, 98 patients were identified. Of these, based on image quality and availability, 32 (38 affected moieties) were selected. Most patients (65%) were girls, diagnosed at age 50 ± 37 months of age. NaC was employed in 64% of patients. Surgical management included 27 radical and 11 partial nephrectomies. Automated volume assessment demonstrated objective response to NaC for unilateral and bilateral tumours (68 ± 20% and 53 ± 39%, respectively), as well as preservation on uninvolved parenchyma with partial nephrectomy (70 ± 46 cm 3 at presentation to 57 ± 41 cm 3 post‐surgery). Conclusion: Volumetric analysis is feasible and allows objective assessment of tumour and parenchyma volume in response to chemotherapy and surgery. Our data show changes after therapy that may be otherwise difficult to quantify. Use of such technology may improve surgical planning and quantification of response to treatment, as well as serving as a tool to predict renal reserve and long‐term changes in renal function.