Author Correction: Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients (Journal of Gastrointestinal Cancer, (2020), 51, 2, (601-609), 10.1007/s12029-019-00291-0)

The original version of this article unfortunately contained a mistake. In the author group section, the correct name of the fourth author is �Reza Ghalehtaki.� The authors apologize for this oversight and for any confusion it may have caused. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Congestive Heart Failure versus Inflammatory Carcinoma in Breast

Inflammatory breast cancer is a rare highly malignant form of breast cancer. Clinical signs and symptoms with histologic examination usually confirm the diagnosis. There are rare reports of breast edema of congestive heart failure which were difficult to differentiate from inflammatory carcinoma. The differential becomes more difficult when congestive heart failure is associated with unilateral breast edema. We present a case of a 70-year-old woman with congestive heart failure associated with unilateral breast edema and skin thickening simulating inflammatory breast carcinoma on mammography

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients

Objectives: The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on magnetic resonance imaging (MRI) to predict neo-adjuvant chemoradiation therapy (nCRT) response in rectal cancer patients. We also aimed to study the effect of Laplacian of Gaussian (LOG) filter on EMLMs predictive performance. Methods: 98 rectal cancer patients were divided into a training (n = 53) and a validation set (n = 45). All patients underwent MRI a week before nCRT. Several features from intensity, shape and texture feature sets were extracted from MR images. SVM, Bayesian network, neural network and KNN classifiers were used individually and together for response prediction. Predictive performance was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC). Results: Patients' nCRT responses included 17 patients with Grade 0, 28 with Grade 1, 34 with Grade 2, and 19 with Grade 3 according to AJCC/CAP pathologic grading. In without preprocessing MR Image the best result was for Bayesian network classifier with AUC and accuracy of 75.2 and 80.9 respectively, which was confirmed in the validation set with an AUC and accuracy of 74 and 79 respectively. In EMLMs the best result was for 4 (SVM.NN.BN.KNN) classifier EMLM with AUC and accuracy of 97.8 and 92.8 in testing and 95 and 90 in validation set respectively. Conclusions: In conclusion, we observed that machine learning methods can used to predict nCRT response in patients with rectal cancer. Preprocessing LOG filters and EL models can improve the prediction process. © 2019 Associazione Italiana di Fisica Medic