This paper discusses five enduring gaps in KT research, including tumor imaging of the kidney: most systems do not go beyond detection and segmentation and can rarely assess the early consequences of renal dysfunction, metastasis risk, treatment appropriateness; deep models are rarely explainable and, therefore, few clinics trust them, and images are rarely converted into actionable decision support. We are striving to create a hybrid and knowledge-infused architecture that combines deep learning to do CT tomography and a rule-based reasoning layer to allow interpretable, consequence-aware, and automatically prioritized clinical advice. This system does strong pre-processing and segmentation, learns radiomics and learned representation, estimates consequence endpoints such as impact on renal functionality, spread probability), and implements a rule-augmented decision engine that aligns its outputs with guideline logic, offers human readable justifications and reveals calibrated uncertainty. The novelty lies in unifying four elements such as high-performing segmentation, consequence prediction, embedded clinical rules, and explainable decision logic within a single end-to-end pipeline that is designed for multi-center validation and bedside interpretability. Deliverables include: a modular hybrid framework; an explainable decision module mapping predictions to recommendations and follow-up intervals; an integration layer for guideline rules; a multi-dataset validation report emphasizing calibration, generalization, and fairness; and open research assets such as code or configs, ablation protocols. Scope focuses on adult renal CT such as multi-phase when available, tumor/cyst segmentation, subtype/grade support, survival/metastasis risk stratification, and clinician-facing explanations; out of scope are intraoperative workflows and non-tomographic primary inputs. Evidence synthesis is summarized in two companion tables: Master Review Table framed with 25 and Master Review Table framed with 45 research articles. The first distills foundational datasets, benchmarks, kidney and tumor segmentation, radiomics baselines, and XAI design principles; the second captures recent challenge-grade segmentation advances, CNN–Transformer hybrids, uncertainty handling, radiogenomics, survival/metastasis modeling, and translational XAI or governance. Together, these tables motivate our design choices like as expected Dice ranges for kidneys and tumors, typical survival C-indices, and data curation or tuning practices and substantiate the need for a rule-augmented, explainable, consequence-first decision pipeline for early kidney tumor management. Clinical Trial Number: Not applicable.
A. et al. (Thu,) studied this question.