Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review

Sushentsev, Nikita; Moreira Da Silva, Nadia; Yeung, Michael; Barrett, Tristan; Sala, Evis; Roberts, Michael; Rundo, Leonardo

doi:10.1186/s13244-022-01199-3

Insights into Imaging

Table 4 Predictive modelling characteristics of studies using traditional machine learning-based semi-automated AI methods

From: Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review

Study	No. of patients	Training set	Validation set	Test set	Algorithm	MRI input	IR	IS	Discriminative features	No. of features used for training	Outcome	Zone	Analysis	Evaluation strategy
Bonekamp [25]	316	183	NR	133	RF	T2WI, ADC, b = 1500	No	Manual	First-order, volume, shape, texture	NR	csPCa vs iPCa or benign lesions	WP or PZ or TZ	Per lesion and per patient	Internal hold-out
Min [26]	280	187	NR	93	LR	T2WI, ADC, b = 1500	No	Manual	Intensity, shape, texture, wavelet	9	csPCa vs iPCa	WP	Per lesion	Internal hold-out
Kwon [27]	344	204	tenfold CV	140	CART, RF, LASSO	T2WI, DWI, ADC, DCE	Rigid	No	Intensity	54	csPCa vs iPCa or benign lesions	PZ or TZ	Per lesion	Internal hold-out
Castillo [28]	107	80%	20% of training (100 random repeats)	20%	LR, SVM, RF, NB, LQDA	T2WI, DWI, ADC	HP^a	Manual	Shape, local binary patterns, GLCM	NR	csPCa vs iPCa	WP	Per lesion, Per patient	Mixed hold-out
Bleker [29]	206	130	NR	76	RF, XGBoost	T2WI, b = 50, b = 400, b = 800, b = 1400, ADC, K^trans	No	Manual	Intensity, texture	NR	csPCa vs iPCa or benign lesions	PZ	Per lesion	Internal hold-out
Li [30]	381	229	NR	152	LR	T2WI, ADC	No	Manual	Intensity, age, PSA, PSAd	15	csPCa vs iPCa or benign lesions	WP	Per lesion	Internal hold-out
Woźnicki [31]	191	151	fivefold CV	40	LR, SVM, RF, XGBoost, CNN	T2WI, ADC	No	Manual	Intensity, shape, PI-RADS, PSAd, DRE	15	csPCa vs iPCa or benign lesions	WP	Per patient	Internal hold-out
Bevilacqua [32]	76	48	threefold CV	28	SVM	ADC, b = 2000	No	Manual	Intensity	10	csPCa vs iPCa	WP	Per lesion	Internal hold-out
Toivonen [33]	62	62	LPOCV	N/A	LR	T2WI, ADC, K^trans, T2 map	No	Manual	Intensity, Sobel, texture	NR	csPCa vs iPCa	WP	Per lesion	LPOCV
Antonelli [34]	164	134	NR	30	PZ: LinR TZ: NB	ADC, DCE	Rigid	Manual	Texture, PSAd	NR	csPCa vs iPCa	PZ or TZ	Per lesion	fivefold CV
Yoo [35]	427	271	48	108	CNN, RF	ADC, DWI	No	No	First-order statistics of deep features	90	csPCa vs iPCa or benign lesions	WP	Per slice, Per patient	tenfold CV
Hiremath [36]	592	368	threefold CV	224	AlexNet or DenseNet and Nomogram	T2WI, ADC	Rigid, affine	Manual	Deep learning imaging predictor, PI-RADS, PSA, gland volume, tumour volume	NR	csPCa vs iPCa or benign lesions	WP	Per patient	External hold-out

ADC, apparent diffusion coefficient; CART, classification and regression trees; CNN, convolutional neural networks; GLCM, grey level co-occurrence matrix; HP, histopathology; IR, image registration; IS, image segmentation; LASSO, least absolute shrinkage and selection operator; LinR, linear regression; LQDA, linear and quadratic discriminant analysis; LR, logistic regression; NB, naïve Bayes; PI-RADS, prostate imaging-reporting and data system; PSA, prostate-specific antigen; PSAd, prostate-specific antigen density; RF, random forests; SVM, support-vector machines
^aHistopathology images registered with T₂-weighted images using specialised software

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