. 2020 Oct 15;8(5):152–162.

Table 2.

Renal cancer review articles summary

Legend	Name/Type of Model	No. of patients	Cancer	Summary	Data Analysis	Implications	References
2-A	CNN	135 for training	Renal Cancer	A deep learning neural network was developed to distinguish between three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using computed tomography images, with regions of interest marked by radiologists. Automated results were trained with biopsy-proven samples.	The network showed 85% accuracy with AUC of 0.9.	ML models that can classify subtypes of cancer from CT images may have an important role in reducing the need for invasive biopsies and in training radiologists to identify further signs of specific diagnoses.	29. Han, S. et al.
2-A	CNN	34 for testing	Renal Cancer		The network showed 85% accuracy with AUC of 0.9.		29. Han, S. et al.
2-B	SVM	59	Renal Cancer	An automated image classification pipeline was created to detect relevant nuclear pleomorphic patterns from histopathologic tissue of patients with clear cell renal cell carcinoma and grade these images using the Fuhrman grading scale.	The results demonstrated a correlation (R = 0.59) between the automated pipeline and an already-established multigene assay-based scoring system.	An automated pipeline that is able to grade histopathologic samples of clear cell renal cell carcinoma into high and low grades has important clinical implications in terms of treatment planning and reducing inter-pathologist variability in grading.	30. Holdbrook, D.A. et al.
2-C	naïve Bayes	231 patients	Renal Cancer	A ML model was developed to predict the Fuhrman grade of clear cell renal cell carcinoma from single- and three-phase CT images. Automated results were confirmed with pathology-proven samples.	The model based on three-phase CT images achieved the best diagnostic performance with AUC = 0.87.	ML has the potential to minimize the risks, resources, and subjectivity associated with biopsy collection and manual pathological analysis for grading of clear cell renal cell carcinoma.	31. Lin, F. et al.
2-C	naïve Bayes	(232 pathologically-proven clear cell renal cell carcinoma lesions)	Renal Cancer				31. Lin, F. et al.
2-D	ANN	175	Renal Cancer	A ML algorithm was developed to predict 36-month survival in patients with renal cell carcinoma using a multitude of clinical prognostic data, including tumor grade, vessel invasion, and pathologic T classification, to input into the network.	The ANN achieved an accuracy of 95%.	AI has the potential to aid in developing personalized treatment plans for patients with renal cancer based on varied prognostic factors and automated ML outcome analyses.	33. Buchner, A. et al.