breast lesions from tomosynthesis images. The conceived DSS takes into account the shape of the lesion and encircles the potential growth pattern of the tumorous regions on the image under test. The article entitled \"Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer\" by [PERSON] et al. has considered the sentinel lymph node status in breast cancer, which is an unbalanced classification issue. The authors adopt machine learning techniques to study the effect of different sampling strategies on both performance and feature stability for the prediction of the sentinel lymph node status in breast cancer. [PERSON] et al., in \"Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers\", have determined 34 routine blood values, adopting a statistical approach to find the most accurate classifier to predict mortality in patients suffering from COVID-19. Moreover, the lethal risk level of these values is determined using threshold approaches. In the paper \"Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models\", [PERSON] et al. evaluated the performance of well-known deep learning models on a larger COVID-19 chest X-ray image dataset for the implementation of a DSS for the accurate diagnosis of the SARS CoV-2 virus. [PERSON] et al. in the study named \"A Decision Support System for Melanoma Diagnosis from Dermoscopic Images\" developed a new pipeline method which uses computer vision procedure for the detection of melanoma in dermoscopic images. The strategy is inspired by clinical practice but adopts original features linked to both geometric properties and chromatic characteristics. Moreover, a statistical study is performed for the evaluation of image quality based on the method's performance. A computer-aided system for the prediction of the risk for second primary skin cancer survivors is developed by [PERSON] et al. in the paper entitled \"Clinical Risk Factor Prediction for Second Primary Skin Cancer: A Hospital-Based Cancer Registry\". The authors identify the clinical characteristics of second primary skin cancer and highlight that age, stage, gender, and involvement of the regional lymph nodes are the most significant impact factors for second primary skin cancer. [PERSON] et al., in the paper \"The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations\", analyzed the behavior of clinicians during the adoption of machine learning-based DSS in the cases of explanation by feature and explanation by example. They underline that healthcare practitioners have different preferences for explanations, so during the development phase of DSSs, designers should select the method in accordance with the users. Moreover, it should be advisable to equip DSSs with several options of explanations for physicians with different clinical expertise and cognitive styles. In the article \"An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods\", [PERSON] et al. have highlighted that in the field of prediction, all the requisite steps/actions must be taken for computational time reduction, in addition to developing procedures with high accuracy. To achieve this aim, the authors propose a combination of statistical methods and machine-learning algorithms. Considering the above, it is clear that this Special Issue highlights several DSS applications to detect a variety of pathologies/disorders by means of different techniques and diagnostic investigations.