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We evaluate the stability of the clinical application of the MAP scoring system based on anatomical features of renal tumour images, explore the relevance of this scoring system to the choice of surgical procedure for patients with limited renal tumours, and investigate the effectiveness of automated segmentation and reconstruction 3D models of renal tumour images based on U-net for interpretative cognitive navigation during laparoscopy Tl stage radical renal tumour cancer surgery. A total of 5 000 kidney tumour images containing manual annotations were applied to the training set, and a stable and efficient full CNN algorithm model oriented to clinical needs was constructed to regionalism and multistructure and to finely automate segmentation of kidney tumour images, output modelling information in STL format, and apply a tablet computer to intraoperatively display the Tl stage kidney tumour model for cognitive navigation. Based on a training sample of MR images from 201 patients with stage Tl renal tumour cancer, an adaptation of the classical U-net allows individual segmentation of important structures such as renal tumours and 3D visualisation to visualise the structural relationships and the extent of tumour invasion at key surgical sites. The preoperative CT and clinical data of 225 patients with limited renal tumours treated surgically at our hospital from August 2011 to August 2012 were retrospectively analysed by three imaging physicians using the MAP scoring system for the total score and the variables R (maximum diameter), E (exogenous/endogenous), N (distance from the renal sinus), A (ventral/dorsal), L (relationship along the longitudinal axis of the kidney), and h (whether in contact with the renal hilum). The score for each variable (contact with the renal hilum) was statistically compared with each other for the three observers. Patients were divided into three groups according to the total score—low, medium, and high—and according to the surgical procedure—radical and partial resection. The correlation between the total score and the score of each variable and the choice of surgical procedure was analysed. The agreement rate of the total score and the score of each variable for all three observers was over 90% ( P  ≤ 0.001). The map scoring system based on the anatomical features of renal tumour imaging was well stabilized, and the scores were significantly correlated with the surgical approach.

U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study