Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

Table 1 Overview of the most widely adopted machine learning algorithms in rectal cancer imaging

Algorithm name	Description
Random forest	An ensemble method that combines multiple decision trees (a class of predictive learning models used in supervised ML) to obtain more accurate results for classification and regression tasks
Support vector machine	A linear approach used mainly for classification problems with the aim to find the best hyper plane which most accurately separate input data into two classes
Logistic regression	A classifier used to obtain the best fitting model for the relationship between multiple predictor variables and a dichotomous outcome
LASSO	A regularized regression method that performs both variable selection and regularization in order to optimally fit the resulting generalized statistical model
Naive Bayes	A classifier relying on the Bayes Theorem to model the probability of an outcome based on the strong (naive) independence assumptions between the features data
Quadratic discriminant analysis	A subtype of Dimensionality Reduction Algorithms that turn high-dimensional data into to low-dimensional data retaining the most significant features of original data for the prediction of the class label
ANN	A subgroup of ML composed of neuronal-like multi-layered networks allowing to automatically extract features without prior labelling and perform complex operations
CNN	As subset of ANN containing multiple computational hidden layers that filter and compute high-dimensional data to enhance the learning of high-level tasks (deep learning)

ANN: Artificial neural network; CNN: Convolutional neural network; LASSO: Least absolute shrinkage and selection operator; ML: Machine learning.

Table 2 Key characteristics of the main studies using radiomics and machine learning algorithms on magnetic resonance images to predict pathologic complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer

Ref.	Study design (n of sites)	Number of patients	Definition of pCR	MRI field strength (n of scanners)	MRI timing	MRI sequence	ML algorithm	Data powering algorithm	Validation	Performance (AUC)
Antunes et al[59], 2020	Retrospective (3)	104	TRG 0 according to AJCC	1.5 and 3 T (> 10)	Pre-nCRT	T2w	RF	Radiomics features	External validation	0.71
Ferrari et al[106], 2019	Retrospective (1)	55	TRG 4 according to Dowrak-Rodel	3 T (1)	Pre-, mid- and post-nCRT	T2w	RF	Radiomics features	Internal validation (train/test split)	0.86
Horvat et al[107], 2018	Retrospective (11)	114	ypT0N0	1,5 and 3 T (4)	Post-nCRT	T2w	RF	Radiomics features	Internal validation (cross-validation)	0.93
Nie et al[108], 2016	Retrospective (1)	48	ypT0N0	3 T (1)	Pre-nCRT	T2w, DWI, pre and post-contrast T1w	ANN	Radiomics features	Internal validation (cross-validation)	0.84
Petkovska et al[109], 2020	Retrospective (11)	1022	ypT0N0	1,5 and 3 T (4)	Pre-nCRT	T2w	SVM	Radiomics and semantic features	Internal validation (train/test split)	0.75
Shaish et al[110], 2020	Retrospective (2)	132	ypT0N0	1,5 and 3 T (multiple3)	Pre-nCRT	T2w	LR	Radiomics features	Internal validation (train/test split)	0.80
Shi et al[111], 2019	Retrospective (1)	51	TRG 0 according to Ryan	3 T (1)	Pre- and mid-Ncrt4	T2w, DWI, pre- and post-contrast T1w	CNN	Radiomics features	Internal validation (cross-validation)	0.83
van Griethuysen et al[60], 2019	Retrospective (2)	133	ypT0/TRG1 according to Mandard	1,5 T (3)	Pre-nCRT	T2w and DWI	LR	Radiomics features	External validation	0.77
Yi et al[112], 2019	Retrospective (1)	134	ypT0N0	1,5 and 3 T (2)	Pre-nCRT	T2w	SVM	Radiomics, clinical and semantic features	Internal validation (train/test split)	0.88

¹< 10% of scans from other institutions.

²All previously included in Horvat et al[107], 2018.

³Inclusion of patients with MRI performed elsewhere but treated at study sites.

⁴Both MRI scans were not available for all patients. In all studies, three-dimensional manual segmentation of the primary tumor was performed to extract radiomic features, except for Shaish et al[110], mesorectal compartment) and van Griethuysen et al[60] (semiautomatic segmentation). ANN: Artificial neural network; CNN: Convolutional neural network; AUC: Area under the receiver operating characteristic curve; DWI: Diffusion-weighted imaging; LR: Logistic regression; ML: Machine learning; MRI: Magnetic resonance imaging; nCRT: Neoadjuvant chemoradiotherapy; RF: Random forest; SVM: Support vector machine; T1w: T1-weighted; T2w: T2-weighted; TRG: Tumor regression grade.

Table 3 Key characteristics of the main studies using radiomics and machine learning algorithms on magnetic resonance images to predict outcome other than pathologic complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer

Ref.	Study design (n of sites)	Number of patients	Prediction task	CT phase (n of CT scanner)	Segmentation method	ML algorithm	Data powering algorithm	Validation	Performance
Bibault et al[85], 2018	Retrospective (3)	99	pCR after nCRT	Unenhanced (3)	Manual – 3D	DNN	Radiomics and clinical features	Internal validation (cross-validation)	AUC: 0.72
Hamerla et al[86], 2019	Retrospective (1)	169	pCR after nCRT	Unenhanced (1)	Manual – 3D	RF	Radiomics features	Internal validation (cross-validation)	Accuracy: 0.87
Yuan et al[87], 2020	Retrospective (1)	91	pCR after nCRT	Unenhanced (1)	Manual – 3D	RF	Radiomics features	Internal validation (train/validation split)	Accuracy: 0.84
Wu et al[90], 2019	Retrospective (1)	102	MSI status	Venous phase - DECT (2)	Manual - 3 2D ROIs for lesion	LR	Radiomics features	Internal validation (train/validation /test split)	AUC: 0.87
Fan et al[91], 2019	Retrospective (1)	100	MSI status	Portal venous phase (2)	Semiautomatic – 3D	NB	Radiomics features	Internal validation (cross-validation)	AUC: 0.75
Wu et al[92], 2020	Retrospective (1)	173	KRAS mutation	Portal venous phase (3)	Manual + DL – single 2D ROI	LR	Radiomics features	Internal validation (train/test split)	C-index: 0.83
Wang et al[94], 2019	Retrospective (1)	411	Prediction of survival	Unenhanced (1)	Manual – 3D	10-F CV	Radiomics and clinical features	Internal validation (cross-validation)	C-index: 0.73

In all studies, three-dimensional manual segmentation of the primary tumor was performed to extract radiomic features, with the exceptions of Alvarez-Jimenez et al[113] (rectal wall), van Griethuysen et al[60] (semiautomatic segmentation) and Yang et al[115] (two-dimensional manual segmentation). ANN: Artificial neural network; AUC: Area under the receiver operating characteristic curve; CNN: Convolutional neural network; DWI: Diffusion-weighted imaging; EMLM: Ensemble machine learning model; GR: Good responders; LASSO: Least absolute shrinkage and selection operator; RF: Random forest; LR: Logistic regression; ML: Machine learning; MRI: Magnetic resonance imaging; QDA: Quadratic discriminant analysis; SVM: Support vector machine; T1w: T1-weighted; T2w: T2-weighted; TRG: Tumor regression grade.

10F-CV: 10-fold cross-validation; CT: Computed tomography; DECT: Dual-energy computed tomography; DNN: Deep neural network; LR: Logistic regression; ML: Machine learning; MSI: Microsatellite instability; NB: Naive Bayes; nCRT: Neoadjuvant chemoradiotherapy; pCR: Pathologic complete response; RF: Random forest.

Table 4 Key characteristics of the main studies using radiomics and machine learning algorithms on computed tomography for v prediction tasks

Ref.	Study design (n of sites)	Number of patients	Prediction task	CT phase (n of CT scanner)	Segmentation method	ML algorithm	Data powering algorithm	Validation	Performance
Bibault et al[85], 2018	Retrospective (3)	99	pCR after nCRT	Unenhanced (3)	Manual – 3D	DNN	Radiomics and clinical features	Internal validation (cross-validation)	AUC: 0.72
Hamerla et al[86], 2019	Retrospective (1)	169	pCR after nCRT	Unenhanced (1)	Manual – 3D	RF	Radiomics features	Internal validation (cross-validation)	Accuracy: 0.87
Yuan et al[87], 2020	Retrospective (1)	91	pCR after nCRT	Unenhanced (1)	Manual – 3D	RF	Radiomics features	Internal validation (train/validation split)	Accuracy: 0.84
Wu et al[90], 2019	Retrospective (1)	102	MSI status	Venous phase - DECT (2)	Manual - 3 2D ROIs for lesion	LR	Radiomics features	Internal validation (train/validation /test split)	AUC: 0.87
Fan et al[91], 2019	Retrospective (1)	100	MSI status	Portal venous phase (2)	Semiautomatic – 3D	NB	Radiomics features	Internal validation (cross-validation)	AUC: 0.75
Wu et al[92], 2020	Retrospective (1)	173	KRAS mutation	Portal venous phase (3)	Manual + DL – single 2D ROI	LR	Radiomics features	Internal validation (train/test split)	C-index: 0.83
Wang et al[94], 2019	Retrospective (1)	411	Prediction of survival	Unenhanced (1)	Manual – 3D	10-F CV	Radiomics and clinical features	Internal validation (cross-validation)	C-index: 0.73

10F-CV: 10-fold cross-validation; CT: Computed tomography; DECT: Dual-energy computed tomography; DNN: Deep neural network; LR: Logistic regression; ML: Machine learning; MSI: Microsatellite instability; NB: Naive Bayes; nCRT: Neoadjuvant chemoradiotherapy; pCR: Pathologic complete response; RF: Random forest.