Nearly one third of patients undergoing neoadjuvant chemoradiotherapy (nCRT) for locally advanced esophageal cancer have a pathologic complete response (pCR) of the primary tumor upon histopathological evaluation of the resection specimen. The primary aim of this study is to develop a model that predicts the probability of pCR to nCRT in esophageal cancer, based on diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography with computed tomography (18F-FDG PET-CT). Accurate response prediction could lead to a patient-tailored approach with omission of surgery in the future in case of predicted pCR or additional neoadjuvant treatment in case of non-pCR. The PRIDE study is a prospective, single arm, observational multicenter study designed to develop a multimodal prediction model for histopathological response to nCRT for esophageal cancer. A total of 200 patients with locally advanced esophageal cancer - of which at least 130 patients with adenocarcinoma and at least 61 patients with squamous cell carcinoma - scheduled to receive nCRT followed by esophagectomy will be included. The primary modalities to be incorporated in the prediction model are quantitative parameters derived from MRI and 18F-FDG PET-CT scans, which will be acquired at fixed intervals before, during and after nCRT. Secondary modalities include blood samples for analysis of the presence of circulating tumor DNA (ctDNA) at 3 time-points (before, during and after nCRT), and an endoscopy with (random) bite-on-bite biopsies of the primary tumor site and other suspected lesions in the esophagus as well as an endoscopic ultrasonography (EUS) with fine needle aspiration of suspected lymph nodes after finishing nCRT. The main study endpoint is the performance of the model for pCR prediction. Secondary endpoints include progression-free and overall survival. If the multimodal PRIDE concept provides high predictive performance for pCR, the results of this study will play an important role in accurate identification of esophageal cancer patients with a pCR to nCRT. These patients might benefit from a patient-tailored approach with omission of surgery in the future. Vice versa, patients with non-pCR might benefit from additional neoadjuvant treatment, or ineffective therapy could be stopped. The article reports on a health care intervention on human participants and was prospectively registered on March 22, 2018 under ClinicalTrials.gov Identifier: NCT03474341 .

Background/Objectives: There is a growing need for efficient prostate MRI protocols due to their increasing use in managing prostate cancer (PCa) and potential inclusion in screening. Deep learning reconstruction (DLR) may enhance MR acquisitions and improve image quality compared to conventional acceleration techniques. This systematic review examines DLR approaches to prostate MRI. Methods: A search of PubMed, Web of Science, and Google Scholar identified eligible studies comparing DLR to conventional reconstruction for prostate imaging. A narrative synthesis was performed to summarize the impact of DLR on acquisition time, image quality, and diagnostic performance. Results: Thirty-three studies showed that DLR can reduce acquisition times for T₂w and DWI imaging while maintaining or improving image quality. It did not significantly affect clinical tasks, such as biopsy decisions, and performed comparably to human readers in PI-RADS scoring and the detection of extraprostatic extension. However, AI models trained on conventional data might be less accurate with DLR images. The heterogeneity in image quality metrics among the studies prevented quantitative synthesis. Discussion: DLR has the potential to achieve substantial time savings in prostate MRI while maintaining image quality, which is especially relevant because of increased MRI demands. Future research should address the effect of DLR on clinically relevant downstream tasks, including AI algorithms’ performances and biopsy decisions, and explore task-specific accelerated protocols for screening, image-guided biopsy, and treatment.

Optical technologies can potentially be used for tissue characterization in, for example, the detection of positive resection margins during cancer surgery. These technologies are based on the principle that light that has undergone interaction with the tissue contains information regarding the composition, morphology, and microvasculature of the tissue which can be used to discriminate different tissue types. Validation of these optical measurements is done by comparing the experimental results with the gold standard.¹ Currently, the gold standard for tissue characterization is an assessment of microscopy sections, which are stained with hematoxylin and eosin (H&E), by a pathologist. However, the tissue in the H&E sections, used for histopathological assessment, is profoundly deformed compared to the appearance of the tissue during the optical measurements.^{2 – 4} These deformations are introduced when the tissue is processed into H&E sections as explained and shown in Fig. 1. Part of these deformations is related to tissue shrinkage due to formalin fixation and tissue compression and stretching when the tissue is embedded into paraffin blocks. Consequently, overlaying digitalized H&E sections over images that reflect optical measurements of the fresh, unfixed tissue is prone to registration errors.^{5 – 8}

Quantitative MRI has the potential to produce imaging biomarkers for the prediction of early response to radiotherapy treatment. In this pilot study, a potential imaging biomarker, the T_1ρ relaxation time, is assessed for this purpose. A T_1ρ sequence was implemented on a 1.5 T MR-linac system, a system that combines an MRI with a linear accelerator for radiation treatment. An agar phantom with concentrations of 1–4% w/w was constructed for technical validation of the sequence. Phantom images were assessed in terms of short-term repeatability and signal-to-noise ratio. Twelve rectal cancer patients, who were treated with 5 × 5 Gy, were imaged on each treatment fraction. Individual changes in the T_1ρ values of the gross tumor volume (GTV) showed an increase for most patients, although a paired t-test comparing values in the GTV from the first to the last treatment fraction showed no statistically significant difference. The phantom measurements showed excellent short-term repeatability (0.5–1.5 ms), and phantom T_1ρ values corresponded to the literature values. T_1ρ imaging was implemented successfully on the MR-linac, with a repeatability comparable to diagnostic systems, although clinical benefit in terms of treatment response monitoring remains to be demonstrated.

Knowledge of patient-specific liver anatomy is key to patient safety during major hepatobiliary surgery. Three-dimensional (3D) models of patient-specific liver anatomy based on diagnostic MRI images can provide essential vascular and biliary anatomical insight during surgery. However, a method for generating these is not yet publicly available. This paper describes how these 3D models of the liver can be generated using open source software, and then subsequently integrated into a sterile surgical environment. The most common image quality aspects that degrade the quality of the 3D models as well possible ways of eliminating these are also discussed. Per patient, a single diagnostic multiphase MRI scan with hepatospecific contrast agent was used for automated segmentation of liver contour, arterial, portal, and venous anatomy, and the biliary tree. Subsequently, lesions were delineated manually. The resulting interactive 3D model could be accessed during surgery on a sterile covered tablet. Up to now, such models have been used in 335 surgical procedures. Their use simplified the surgical treatment of patients with a high number of liver metastases and contributed to the localization of vanished lesions in cases of a radiological complete response to neoadjuvant treatment. They facilitated perioperative verification of the relationship of tumors and the surrounding vascular and biliary anatomy, and eased decision-making before and during surgery.