Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

Table 5

Different clinical applications of radiomic models (features) in prostate cancer.

References	Case numbers	Radiomic method	Results
[147]	206	MRI	The phenotype of clinically significant peripheral zone PCa lesions could be predicted by using radiomic features
[148]	381	MRI	Radiomic prediction model had an improved diagnostic ability when compared with the clinical model
[149]	117	MRI	Radiomic model was adaptive to detect dominant intraprostatic lesions in patients with PCa
[150]	30	MRI	Quantitative radiomic features based on MRI radiomics could be utilized to predict the localization of PCa
[151]	50	US	Quantitative radiomic features based on US radiomics could be utilized to predict the localization of PCa
[144]	381	MRI	MRI-based radiomic models had a reliable ability to distinguish PCa with non-PCa patients as well as assess the tumor aggressiveness
[153]	73	MRI	Radiomic features had the potential to predict risk stratification of PCa
[156]	23	MRI	The focal treatment plans formed by using the framework were decreased in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions
[158]	62	MRI	Radiomic features had good classification performance for Gleason score of patients in PCa
[159]	71	MRI	Radiomic features had the potential to predict the prognosis of PCa
[160]	107	MRI	Radiomic features were predictive of biochemical recurrence after prostatectomy in PCa
[161]	120	MRI	Radiomic features can be predictive of PCa BCR and may help identify men who would benefit from adjuvant therapy
[162]	91	MRI	MRI-based radiomics could predict BCR of localized PCa after radiation therapy
[163]	195	MRI	MRI-based radiomic models had the potential to predict BCR of high-risk PCa