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Background: Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer. Materials and methods: A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy. Results: Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01). Conclusion: Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment. [ABSTRACT FROM AUTHOR]

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 31-year-old female patient that presented postpartum abdominal pain with fever and was diagnosed with hepatic adenoma. Topics discussed include brief details about the medical history and pregnancy of the patient prior to hospital admission, the results of the physical and laboratory examinations performed such as blood tests and urinalysis, and the patient outcomes recorded following treatment.

This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy. [ABSTRACT FROM AUTHOR]

Imaging plays an important role in the diagnosis and treatment of women with uterine cervical and endometrial cancers. Quantitative imaging, through MRI, PET/CT, and hybrid PET/MRI, allows for characterization of primary tumors beyond anatomic and qualitative descriptors. MRI diffusion-weighted imaging (DWI) yields an apparent diffusion coefficient (ADC), which can be applied in both the pre-and post-treatment assessment of uterine tumors. PET/CT assesses metabolic activity, and measurement of tumor standardized uptake value (SUV) is a useful metric in the staging of uterine malignancies. Hybrid PET/MRI is an emerging modality that combines the soft tissue contrast of MRI with the molecular imaging capability of PET. This review provides an overview of these quantitative imaging modalities, and their current and potential roles in the assessment of uterine cervical and cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: In breast cancer, the leading cancer type and the main cause of cancer death in women, achieving pathological complete response after neoadjuvant chemotherapy has been shown to be associated with prolonged overall survival. Hence, the correct assessment and the potential prediction of therapy response have recently become the focus of research. In this study, we predicted pathological complete response prior to neoadjuvant system therapy using 18F-FDG PET/MRI radiomics analysis of the breast. Hence, simultaneous 18F-FDG PET/MRI may enable a more individualized and targeted approach to treatment as well as pretherapeutic patient stratification. Background: The aim of this study was to assess whether multiparametric 18F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of 73 female patients (mean age 49 years; range 27–77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous 18F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model. Results: The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2− group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets. Conclusion: 18F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2− receptor status. [ABSTRACT FROM AUTHOR]

5 Lee SI, Atri M. 2018 FIGO staging system for uterine cervical cancer: Enter cross-sectional imaging. Editorial for "A Multicenter Study on Preoperative Assessment of Lymphovascular Space Invasion in Early-Stage Cervical Cancer Based on Multimodal MR Radiomics" Cervical cancer is a prevalent disease responsible for a substantial burden, being the fourth most common cancer in women globally.[1] Although established primary and secondary prevention practices exist with human papillomavirus (HPV) vaccination and Papanicolaou smear screening, respectively, this disease is predicted to claim 13,960 lives in the United States alone in 2023.[2] Moreover, significant disparities persist. [Extracted from the article]

Simple Summary: Over the past few years, radiomics-based tissue characterization has demonstrated its potential for non-invasive prediction of the genetic profile and grading in cerebral gliomas using multiparametric MRI. The aim of our study was to investigate the feasibility and diagnostic accuracy of a fully automated radiomics analysis based on a simplified MR protocol derived from various scanner systems to prospectively ease the transition of radiomics-based non-invasive tissue sampling into clinical practice. Using an MRI with non-contrast and post-contrast T1-weighted sequences and FLAIR, our workflow automatically predicts the IDH1/2 mutation, the ATRX expression loss, the 1p19q co-deletion and the MGMT methylation status. It also effectively differentiates low-grade from high-grade gliomas. In summary, the present study demonstrated that a fully automated prediction of grading and the genetic profile of cerebral gliomas could be performed with our proposed method using a simplified MRI protocol that is robust to variations in scanner systems, imaging parameters and field strength. Objective: The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. Methods: MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. Results: The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. Conclusion: This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding. [ABSTRACT FROM AUTHOR]

To implement a new daily peer learning (PL) conference which incorporates faculty and trainees within the abdominal imaging division of a large academic medical center, and to determine participants' level of satisfaction and preference over the pre-existing peer review (PR) model. We replaced our pre-existing PR-based tool with a daily hour-long case-based PL teaching conference over a 3-month pilot period. Faculty and trainees were surveyed about their experience at the end of the pilot period. A total of 711 cases were logged during the pilot period (median 9 cases per day). We received 30 survey responses from a total of 48 eligible participants. Survey responses from both faculty and trainees on the new PL conference were overwhelmingly favorable, including unanimous support for permanently replacing the existing PR tool with the new PL conference. Our successful pilot of a daily PL conference replacing an existing PR tool adds to the growing body of evidence of radiologists strongly supporting PL based processes over PR. Our paradigm of actively involving trainees within the process can serve as a model for other institutions. • Peer learning is rapidly increasing in popularity as an alternative to traditional peer review. • Surveyed radiologists unanimously preferred participating in peer learning rather than peer review. • Trainees enjoyed participating in peer learning conferences and found the experience to be educational. [ABSTRACT FROM AUTHOR]

Peritoneal malignancies encompass a diverse range of tumors originating within the peritoneum, including primary tumors such as mesothelioma and primary serous peritoneal carcinoma or secondary tumors resulting from the spread of cancers from gastrointestinal, gynecological, and extra-abdominal sources. The traditional approach of palliative care for these malignancies is being replaced by a multimodal strategies that integrates surgery with systemic or intraperitoneal chemotherapy. Notably, cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy has shown significant improvements in survival rates. Imaging is crucial in the multidisciplinary management of these tumors, aiding in diagnosis, staging, restaging, and monitoring therapy response. It is also vital for appropriate patient selection, using the acronym “PAUSE”, which involves assessing tumor burden via the peritoneal carcinomatosis index, evaluating patients pre- and post-therapy, detecting complications following therapy, and predicting treatment outcomes. This review explores the imaging manifestations of peritoneal malignancies, distinguishing them from various mimics, and underscores the importance of imaging modalities such as CT, MRI, PET/CT, and PET/MRI in effective decision-making and management. [ABSTRACT FROM AUTHOR]