New Explainable Deep CNN Design for Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy.

Purpose: To reduce breast tumor size before surgery, Neoadjuvant Chemotherapy (NAC) is applied systematically to patients with local breast cancer. However, with the existing clinical protocols, it is not yet possible to have an early prediction of the effect of chemotherapy on a breast tumor. Predicting the response to chemotherapy could reduce toxicity and delay effective treatment.

Neo-CheckRay: radiation therapy and adenosine pathway blockade to increase benefit of immuno-chemotherapy in early stage luminal B breast cancer, a randomized phase II trial.

Background: Residual breast cancer after neo-adjuvant chemotherapy (NACT) predicts disease outcome and is a surrogate for survival in aggressive breast cancer (BC) subtypes. Pathological complete response (pCR) rate, however, is lower for luminal B BC in comparison to the triple negative (TNBC) and HER2+ subtypes. The addition of immune checkpoint blockade (ICB) to NACT has the potential to increase pCR rate but is hampered by the lower immunogenicity of luminal B BC.

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

Purpose: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder's patients.

Prognostic value of a three-scale grading system based on combining molecular imaging with Ga-DOTATATE and F-FDG PET/CT in patients with metastatic gastroenteropancreatic neuroendocrine neoplasias.

We investigated on the added prognostic value of a three-scale combined molecular imaging with Ga-DOTATATE and F-FDG PET/CT, (compared to Ki-67 based histological grading), in gastroenteropancreatic neuroendocrine neoplasia patients. 85 patients with histologically proven metastatic gastroenteropancreatic neuroendocrine neoplasias, who underwent combined PET/CT imaging were retrospectively evaluated. Highest Ki-67 value available at time of F-FDG PET/CT was recorded.

Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI.

Background: Early prediction of nonresponse is essential in order to avoid inefficient treatments.

Purpose: To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.

Study Type: This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.

The impact of breast MRI workup on tumor size assessment and surgical planning in patients with early breast cancer.

Background: The size and focality of the primary tumor in breast cancer (BC) influence therapeutic decision making. The purpose of this study was to evaluate whether preoperative breast magnetic resonance imaging (MRI) is helpful for the assessment of tumor size and surgical planning in early BC.

Methods: We performed a retrospective review of a prospectively collected database of 174 patients treated at a single institution for invasive BC who had complete documentation of the tumor size from mammography (MMG), ultrasonography (US), and MRI.

A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images.

Purpose: This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method.

Methods: PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy.

Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status.

Background: Validation of new biomarkers is essential for the early evaluation of neoadjuvant treatments.

Purpose: To determine whether measurements of total choline (tCho) by 1H spectroscopy could predict morphological or pathological complete response (pCR) of neoadjuvant treatment and whether breast cancer subgroups are related to prediction accuracy.

Study Type: Prospective, nonrandomized, monocentric, diagnostic study.

Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy.

Objectives: To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups.

Methods: Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2).

Removal of Plaque and Stent Artifacts in Subtraction CT Angiography Using Nonrigid Registration and a Volume Penalty.

Computed tomography angiography (CTA) is an established tool for vascular imaging. However, high-intense structures in the contrast image can seriously hamper luminal visualisation. This can be solved by subtraction CTA, where a native image is subtracted from the contrast image.

Computer-aided detection of colonic polyps using low-dose CT acquisitions.

Rationale And Objectives: This report proposes an alternative method for the automatic detection of colonic polyps that is robust enough to be directly applicable on low-dose computed tomographic data.

Materials And Methods: The polyp modeling process takes into account both the gray-level appearance of polyps (intensity profiles) and their geometry (extended Gaussian images). Spherical harmonic decompositions are used for comparison purposes, allowing fast estimation of the similarity between a candidate and a set of previously computed models.

Plaque and stent artifact reduction in subtraction CT angiography using nonrigid registration and a volume penalty.

Computed tomography angiography (CTA) is an established tool for vessel imaging. Yet, high-intense structures in the contrast image can seriously hamper luminal visualisation. This can be solved by subtraction CTA, where a native image is subtracted from the contrast image.

Computer aided detection for low-dose CT colonography.

The paper describes a method for automatic detection of colonic polyps, robust enough to be directly applied to low-dose CT colonographic datasets. Polyps are modeled using gray level intensity profiles and extended Gaussian images. Spherical harmonic decompositions ensure an easy comparison between a polyp candidate and a set of polypoid models, found in a previously built database.