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©The Author(s) 2021.
World J Gastroenterol. Aug 28, 2021; 27(32): 5306-5321
Published online Aug 28, 2021. doi: 10.3748/wjg.v27.i32.5306
Published online Aug 28, 2021. doi: 10.3748/wjg.v27.i32.5306
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) |
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 |
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 |
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 |
- Citation: Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321
- URL: https://www.wjgnet.com/1007-9327/full/v27/i32/5306.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i32.5306