Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT

Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural network behavior. This study aimed at assessing the feasibility of regression concept activation to explain detection and classification of multi-modal volumetric data. Proof-of-concept was demonstrated in metastatic prostate cancer patients imaged with positron emission tomography/computed tomography (PET/CT). Multi-modal volumetric concept activation was used to provide global and local explanations. Sensitivity was 80% at 1.78 false positive per patient. Global explanations showed that detection focused on CT for anatomical location and on PET for its confidence in the detection. Local explanations showed promise to aid in distinguishing true positives from false positives. Hence, this study demonstrated feasibility to explain detection and classification of multi-modal volumetric data using regression concept activation.Comment: Accepted as: Kraaijveld, R.C.J., Philippens, M.E.P., Eppinga, W.S.C., J\"urgenliemk-Schulz, I.M., Gilhuijs, K.G.A., Kroon, P.S., van der Velden, B.H.M. "Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT." MICCAI workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC), 202

Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI : a feasibility study

We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests.
 
 From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (<i>P</i> < 0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor (ER) subtype tumors (<i>P</i> < 0.0001) and the triple-negative (TN) subtype tumors (<i>P</i>=0.0039), but not for tumors of the HER2 subtype (<i>P</i>=0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer.&#13

Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI : a feasibility study

We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests.
 
 From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (<i>P</i> < 0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor (ER) subtype tumors (<i>P</i> < 0.0001) and the triple-negative (TN) subtype tumors (<i>P</i>=0.0039), but not for tumors of the HER2 subtype (<i>P</i>=0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer.&#13

Feasibility of geometrical verification of patient set-up using body contours and computed tomography data.

BACKGROUND AND PURPOSE: Body contours can potentially be used for patient set-up verification in external-beam radiotherapy and might enable more accurate set-up of patients prior to irradiation. The aim of this study is to test the feasibility of patient set-up verification using a body contour scanner. MATERIAL AND METHODS: Body contour scans of 33 lung cancer and 21 head-and-neck cancer patients were acquired on a simulator. We assume that this dataset is representative for the patient set-up on an accelerator. Shortly before acquisition of the body contour scan, a pair of orthogonal simulator images was taken as a reference. Both the body contour scan and the simulator images were matched in 3D to the planning computed tomography scan. Movement of skin with respect to bone was quantified based on an analysis of variance method. RESULTS: Set-up errors determined with body-contours agreed reasonably well with those determined with simulator images. For the lung cancer patients, the average set-up errors (mm)+/-1 standard deviation (SD) for the left-right, cranio-caudal and anterior-posterior directions were 1.2+/-2.9, -0.8+/-5.0 and -2.3+/-3.1 using body contours, compared to -0.8+/-3.2, -1.0+/-4.1 and -1.2+/-2.4 using simulator images. For the head-and-neck cancer patients, the set-up errors were 0.5+/-1.8, 0.5+/-2.7 and -2.2+/-1.8 using body contours compared to -0.4+/-1.2, 0.1+/-2.1, -0.1+/-1.8 using simulator images. The SD of the set-up errors obtained from analysis of the body contours were not significantly different from those obtained from analysis of the simulator images. Movement of the skin with respect to bone (1 SD) was estimated at 2.3 mm for lung cancer patients and 1.7 mm for head-and-neck cancer patients. CONCLUSION: Measurement of patient set-up using a body-contouring device is possible. The accuracy, however, is limited by the movement of the skin with respect to the bone. In situations where the error in the patient set-up is relatively large, it is possible to reduce these errors using a computer-aided set-up technique based on contour informatio

Agreement between the biopsy-stage and excision-stage for estrogen receptor-status.

<p>Agreement between the biopsy-stage and excision-stage for estrogen receptor-status.</p