BackgroundTo assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.MethodsSixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.ResultsA model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.ConclusionPre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

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Current treatments for fibroids are mainly surgical and expensive, so alternatives need to be found. It is, therefore, vital to develop and evaluate alternatives to surgical procedures, especially when fertility preservation is the goal. Selective progesterone receptor modulators (SPRMs) are synthetic compounds that have either an agonistic or antagonistic impact on target tissues determined by their binding to progesterone receptors. Their mixed activity depends on recruitment of cofactors that regulate transcription along so‐called genomic pathways, as well as nongenomic interactions with other signaling pathways. There is no doubt that surgery remains indicated in some instances, but we must now establish whether use of SPRMs (notably ulipristal acetate) allows less invasive surgery or even complete avoidance of surgery. Long‐term intermittent administration of ulipristal acetate will undoubtedly change our approach to the management of uterine fibroids according to the International Federation of Gynecology and Obstetrics classification, which provides a comprehensive basis for different treatment options. When considering less invasive techniques (uterus‐sparing options like myomectomy), the choice is guided by the size, number and location of fibroids, as well as the personal experience of the gynecologist and available equipment. There is now a growing body of evidence pointing to the crucial role of progesterone pathways in the pathophysiology of uterine fibroids. SPRMs should, therefore, be considered an alternative to surgical therapy, or at least an adjunct to surgery, as illustrated in the algorithms. © 2019 Japan Society of Obstetrics and Gynecology

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

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