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Background/Objectives: Current guidelines recommend Cisplatin/Gemcitabine/Durvalumab as first-line treatment for inoperable or recurrent cholangiocarcinoma (CCA). Molecular tumor boards (MTB) have the expertise to support organ-specific tumor boards with evidence-based treatment recommendations for subsequent lines of treatment, based on genomic tumor data and scientific evidence. This study evaluates the adoption of an MTB at a comprehensive cancer center in Germany and whether actionable genetic alterations are associated with specific imaging phenotypes. Methods: Patients with CCA referred to MTB were enrolled from May 2019 to September 2021. For comparison, a cohort of patients from a second center was included. Data on treatment recommendations, regimens, and survival were collected from prospective registries. Baseline and follow-up contrast-enhanced CT were analyzed according to RECIST 1.1. The chi-square test and t-test were used to compare categorical and continuous variables. Results: 583 patients were referred to the MTB, and 92 patients (47 female/51%) with a mean age of 60.3 ± 11.2 were referred for CCA treatment. 65/92 patients harbored 1–3 targetable mutations. Liver metastases were more frequently observed in patients with targetable mutations (84% vs. 62%). Metastasis to the liver and lung was associated with increased sums of diameters (93 mm and 111 mm vs. 40/73 mm in patients with no liver/lung metastasis). The number of metastases in individual organs was unrelated to treatment targets. Follow-up was available for 25 patients with a median time until imaging progression of 23 weeks. Progression occurred as target progression in 63%, nontarget progression in 13%, and appearance of new lesions in 63%. Conclusions: Most patients with CCA harbored targetable mutations, some were related to disease patterns on imaging. The pattern of treatment response and progression was as diverse as the metastatic spread. [ABSTRACT FROM AUTHOR]

Simple Summary: The Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 are the current standard in assessing tumor dynamics in cross-sectional imaging. Although they provide standardized criteria, several sources of variability remain, such as the susceptibility of manual measurements to interobserver variability, especially when defining the tumor margins in irregularly shaped lesions, or the limitations of diameter-based measurements when assessing changes in the sizes of non-spherical tumors. Algorithms for automated segmentation may offer an opportunity to reduce this variability, provided that they can be implemented into the routine workflow without compromising the reading time. We evaluated a convolutional neural network (CNN)-based algorithm for fully automated lesion tracking and segmentation in longitudinal CT studies and subsequent RECIST 1.1 evaluation and confirmed that its automated diameter and volume measurements of preselected target lesions are reliable and can accelerate RECIST evaluations. Objectives: To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists. Methods: Baseline and follow-up CTs of patients with stage IV melanoma (n = 58) was investigated in a retrospective reading study. Three radiologists performed manual measurements of metastatic lesions. Fully automated segmentations were generated, and diameters and volumes were computed from the segmentation results, with subsequent RECIST 1.1 evaluation. We measured (1) the intra- and inter-reader variability in the manual diameter measurements, (2) the agreement between manual and automated diameter measurements, as well as the resulting RECIST 1.1 categories, and (3) the agreement between the RECIST 1.1 categories derived from automated diameter measurement compared to automated volume measurements. Results: In total, 114 target lesions were measured at baseline and follow-up. The intraclass correlation coefficients (ICCs) for the intra- and inter-reader reliability of the diameter measurements were excellent, being >0.90 for all readers. There was moderate to almost perfect agreement when comparing the timepoint response category derived from the mean manual diameter measurements from all three readers with those derived from automated diameter measurements (Cohen's k 0.67–0.76). The agreement between the manual and automated volumetric timepoint responses was substantial (Fleiss' k 0.66–0.68) and that between the automated diameter and volume timepoint responses was substantial to almost perfect (Cohen's k 0.81). Conclusions: The automated diameter measurement of preselected target lesions in follow-up CT is reliable and can potentially help to accelerate RECIST evaluation. [ABSTRACT FROM AUTHOR]

Simple Summary: Malignant peripheral nerve sheath tumors (MPNSTs) are a diagnostic and therapeutic challenge requiring interdisciplinary teamwork. With this study, we aimed to investigate the perioperative outcomes in patients with these tumors. We demonstrated that an increase in the levels of SUVs on preoperative [18F]FDG PET and large tumor volumes may be a predictive marker for highly proliferative tumors with a poor prognosis, particularly in patients with underlying Neurofibromatosis type 1. Surgery for MPNSTs can improve symptoms, particularly medication-resistant pain, and should also be considered in advanced disease for symptom control/improvement. Background/Objectives: This retrospective observational study aimed to investigate the perioperative outcome in Malignant Peripheral Nerve Sheath Tumors (MPNSTs) with and without relation to Neurofibromatosis Type 1 (NF1) and to detect possible influencing factors. Methods: Clinical reports, histopathological evaluations, imaging, and treatment characteristics were reviewed in 35 operated MPNSTs in 33 patients. Possible predictive valuables included disease type, preoperative tumor volume, SUV and MIB-1 proliferation index, resection margins, the presence of metastasis, and whether radio-/chemotherapy was received. Results: Patients with NF1 were younger (mean age: 29 ± 13, 8–54 years) than sporadic cases (mean age: 45 ± 13, 24–67 years) and exhibited significantly larger preoperative tumor volumes (mean 299 vs. 18 cm3, p = 0.048). Most tumors were located in the facial/cervical/neck area (34%, n = 12), followed by the trunk (31%, n = 11), lower extremity (17%, n = 6), upper extremity (14%, n = 5), and intraspinal area (3%, n = 1). NF1-associated MPNSTs appeared predominantly on the trunk (39%) and sporadically in the facial/cervical/neck area (50%). Complete resection was possible in 66% and an improvement in or stability of function was achieved in most cases (motor 69%, sensory 74%), as well as a decrease in pain intensity (63%). NF1-associated MPNSTs exhibited more severe pain scores (median VRS scale 2, p = 0.002) compared to sporadic tumors (median VRS scale 0.5). Sporadic MPNSTs located at the head/facial/brachial plexus and upper extremities exhibited better preoperative functions compared to those on the lower extremities. In 12 cases with available [18F]FDG PET, the mean preoperative SUV (9.8 ± 7.2) positively correlated with the mean maximum MIB-1 index (34 ± 26%, p = 0.005) and the mean preoperative tumor volume (474.7 ± 68.6 cm3, p = 0.047). The overall survival (OS) was significantly longer in tumors with higher resection extents (R0, p = 0.01) and without accompanying metastasis (p = 0.046), and tended to be longer, but not significantly so, in sporadic MPNSTs. In six and seven tumors, with R1/R2 resection margins and present metastasis, respectively, solid or combined neo-/adjuvant radio-/chemotherapy led to a significantly shorter OS (p = 0.014). Conclusions: NF1-associated MPNSTs have larger tumor volumes, higher SUVs and MIB-1 proliferation indices, and a shorter overall survival period. Nevertheless, surgery can improve symptoms, particularly medication-resistant pain, and should also be considered in advanced disease for symptom control/improvement. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence. [ABSTRACT FROM AUTHOR]

Simple Summary: The incidence of metastatic melanoma is rising, making it imperative to identify patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker, using segmentations from 146 baseline and 146 first follow-up CT scans, to predict best overall response, progression-free survival, and overall survival across various immunotherapies. We volumetrically segmented the total tumour load, excluding cerebral metastases. This study also examined whether reducing the number of segmented metastases per patient affects predictive accuracy. The findings suggest that delta radiomics could enhance the prediction of best overall response, progression-free survival, and overall survival in metastatic melanoma patients undergoing first-line immunotherapy. Although volumetric whole tumour load segmentation is complex, it may provide predictive benefits. Background: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. Methods: The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. Results: The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. Conclusions: The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous. [ABSTRACT FROM AUTHOR]

Differential diagnosis and therapy of heterogeneous breast tumors poses a major clinical challenge. To address the need for a comprehensive, noninvasive strategy to define the molecular and functional profiles of tumors in vivo, we investigated a novel combination of metabolic PET and diffusion-weighted (DW)-MRI in the polyoma virus middle T antigen transgenic mouse model of breast cancer. The implementation of a voxelwise analysis for the clustering of intra- and intertumoral heterogeneity in this model resulted in a multiparametric profile based on [(18)F]Fluorodeoxyglucose ([(18)F]FDG)-PET and DW-MRI, which identified three distinct tumor phenotypes in vivo, including solid acinar, and solid nodular malignancies as well as cystic hyperplasia. To evaluate the feasibility of this approach for clinical use, we examined estrogen receptor-positive and progesterone receptor-positive breast tumors from five patient cases using DW-MRI and [(18)F]FDG-PET in a simultaneous PET/MRI system. The postsurgical in vivo PET/MRI data were correlated to whole-slide histology using the latter traditional diagnostic standard to define phenotype. By this approach, we showed how molecular, structural (microscopic, anatomic), and functional information could be simultaneously obtained noninvasively to identify precancerous and malignant subtypes within heterogeneous tumors. Combined with an automatized analysis, our results suggest that multiparametric molecular and functional imaging may be capable of providing comprehensive tumor profiling for noninvasive cancer diagnostics. Cancer Res; 76(18); 5512-22. ©2016 AACR.