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The aim of this study was to develop and validate a decision support model using data mining algorithms, based on morphologic features derived from MRI images, to discriminate between complete responders (CR) and non-complete responders (NCR) patients after neoadjuvant chemoradiotherapy (CRT), in a population of patients with locally advanced rectal cancer (LARC).Two populations were retrospectively enrolled: group A (65 patients) was used to train a data mining decision tree algorithm whereas group B (30 patients) was used to validate it. All patients underwent surgery; according to the histology evaluation, patients were divided in CR and NCR. Staging and restaging MRI examinations were retrospectively analysed and seven parameters were considered for data mining classification. Five different classification methods were tested and evaluated in terms of sensitivity, specificity, accuracy and AUC in order to identify the classification model able to achieve the best performance. The best classification algorithm was subsequently applied to group B for validation: sensitivity, specificity, positive and negative predictive value, accuracy and ROC curve were calculated. Inter and intra-reader agreement were calculated.Four features were selected for the development of the classification algorithm: MRI tumor regression grade (MR-TRG), staging volume (SV), tumor volume reduction rate (TVRR) and signal intensity reduction rate (SIRR). The decision tree J48 showed the highest efficiency: when applied to group B, all the CR and 18/21 NCR were correctly classified (sensitivity 85.71%, specificity 100%, PPV 100%, NPV 94.2%, accuracy 95.7%, AUC 0.833). Both inter- and intra-reader evaluation showed good agreement (κ 0.6).The proposed decision support model may help in distinguishing between CR and NCR patients with LARC after CRT.

To compare CT and Texture features of liver metastases in Pancreatic Neuroendocrine Tumors (PNETs) and in Non-Pancreatic Neuroendocrine Tumors (NPNETs) according to tumor grading, overall survival (OS), time to progression (TTP) and Ki67 index.23 patients with PNETs and 25 patients with NPNETs affected by liver metastases were compared. The lesions were G1 and G2 according to WHO classification of tumors. Texture parameters (Mean, Standard Deviation, Entropy, Kurtosis, Skewness, Mean of Positive Pixel) at different spatial scale image filtration (SSF) were evaluated in both arterial and portal phase using a dedicated software for volumetric analysis. All CT images were acquired before the beginning of any medical treatment.The following significant results (P 0.05) were found: in the arterial phase for value of Skewness between PNETs G2 and NPNETs G2; in the portal phase between PNETs versus NPNETs, PNETs G1 versus NPNETs G1, PNETs G2 versus NPNETs G2; value of Mean in portal phase in PNETs vs NPNETs. Regarding PNETs, a P 0.05 was found in: inverse correlation between Entropy and TTP; direct correlation between Mean and OS; correlating Kurtosis and high risk of death; correlating Skewness and low risk of death. Regarding NPNETs, P 0.05 was found in: inverse correlation between Entropy and OS; correlating Entropy and high risk of dying.This study shows that CT texture features are significantly different in PNETs from NPNETs. Additionally, textural features such as Entropy, Kurtosis and Skewness, were found to have significant correlation with higher mortality risk.