DWI may identify biological heterogeneity of breast neoplasms. • ADC values vary significantly according to biological features of breast cancer. • Compared with other types, HER2-enriched tumours show highest median ADC value. • Knowledge of biological heterogeneity of breast neoplasm may improve imaging interpretation.

Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15-30% of cases, therefore it would be useful to assess if pretreatment 18 F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC.Methods: 52 patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumourtumor regression grade (TRG) as follows: 22 as pR+ (9 with TRG=1, 13 with TRG=2) and 30 as pR-(16 with TRG=3, 13 with TRG=4 and 1 with TRG=5). First order parameters and 21 second order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semiautomatically segmented tumourtumors on T2-w MRI, ADC maps and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models. Results:In the mono-parametric approach PET homogeneity reached the maximum AUC (0.77; sensitivity=72.7% and specificity=76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing 6 second-order texture features (five from PET and one from T2-w MRI) yields the highest predictivity in distinguish between pR+ and pR-patients (AUC=0.86; sensitivity=86% and specificity=83% at the Youden index). Conclusions:If preliminary results of this study arewill be confirmed, pretreatment PET and MRI images could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.

Purpose: To describe and test a new fully automatic lesion detection system for breast DCE-MRI.Materials and Methods: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrastenhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (systemdetected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.Results: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). Conclusion:The system showed promising results on MR datasets obtained from different scanners producing fatsat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

We studied whether dynamic contrast-enhanced MRI (DCE-MRI) could identify histopathological characteristics of breast cancer. Seventy-five patients with breast cancer underwent DCE-MRI followed by core biopsy. DCE-MRI findings were evaluated following the scoring system published by Fischer in 1999. In this scoring system, five DCE-MRI features, three morphological (shape, margins, enhancement kinetic) and two functional (initial peak of signal intensity (SI) increase and behavior of signal intensity curve), are defined by 14 parameters. Each parameter is assigned points ranging from 0 to 1 or 0 to 2, with higher points for those that are more likely to be associated with malignancy. The sum of all the points defines the degree of suspicion of malignancy, with a score 0 representing the lowest and 8 the highest degree of suspicion. Associations between DCE-MRI features and tumor histopathological characteristics assessed on core biopsies (histological type, grading, estrogen and progesterone receptor status, Ki67 and HER2 status) were studied by contingency tables and logistic regression analysis. We found a significant inverse association between the Fischer's score and HER2-overexpression (odds ratio-OR 0.608, p = 0.02). Based on our results, we suggest that lesions with intermediate-low suspicious DCE-MRI parameters may represent a subset of tumor with poor histopathological characteristics.