Exploring the State of Cancer Imaging Research in Africa.

INTRODUCTION: The growing cancer burden in Africa demands urgent action. Medical imaging is crucial for cancer diagnosis and management and is an essential enabler of precision medicine. To understand the readiness for quantitative imaging analysis to support cancer management in Africa, we analyzed the utilization patterns of imaging modalities for cancer research across the continent. METHODS: We retrieved articles by systematically searching PubMed, using a combination of search terms {"Neoplasm"} AND {"Radiology" or "Diagnostic imaging" or "Radiography" or "Interventional Radiology" or "Radiotherapy" or "Radiation Oncology"} AND {Africa∗ or 54 African countries}. Articles describing cancer diagnosis or management in humans with the utilization of imaging were included. Exclusion criteria were review articles, non-English articles, publications before 2000, noncancer diagnoses, and studies conducted outside Africa. RESULTS: The analysis of diagnostic imaging in Africa revealed a diverse utilization pattern across different cancer types and regions. The literature search identified 107 publications on cancer imaging in Africa. The studies were carried out in 19 African countries on 12 different cancer types with 6 imaging modalities identified. Most cancer imaging research studies used multiple imaging modalities. Ultrasound was the most used distinct imaging modality and MRI was the least frequently used. Most research studies originated from Nigeria, South Africa, and Egypt. CONCLUSION: We demonstrate substantial variability in the presence of imaging modalities, widespread utilization of ultrasonography, and limited availability of advanced imaging modalities for cancer research.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Photon-counting detector CT allows significant reduction in radiation dose while maintaining image quality and noise on non-contrast chest CT.

Purpose: To investigate if clinical non-contrast chest CT studies obtained with PCD CT using much lower radiation exposure can achieve the same image quality as with the currently established EID protocol. Materials/methods: A total of seventy-one patients were identified who had a non-contrast chest computed tomography (CT) done on PCD CT and EID CT scanners within a 4-month interval. Five fellowship trained chest radiologists, blinded to the scanner details were asked to review the cases side-by-side and record their preference for images from either the photon-counting-detector (PCD) CT or the energy-integrating detector (EID) CT scanner. Results: The median CTDIvol for PCD-CT system was 4.710 mGy and EID system was 7.80 mGy (p < 0.001). The median DLP with the PCD-CT was 182.0 mGy.cm and EID system was 262.60 mGy.cm (p < 0.001). The contrast to noise ratio (CNR) was superior on the PCD-CT system 59.2 compared to the EID-CT 53.3; (p < 0.001). Kappa-statistic showed that there was poor agreement between the readers over the image quality from the PCD and EID scanners (κ = 0.19; 95 % CI: 0.12 - 0.27; p < 0.001). Chi-square analysis revealed that 3 out of 5 readers showed a significant preference for images from the PCDCT (p ≤ 0.012). There was no significant difference in the preferences of two readers between EID-CT and PCD-CT images. Conclusion: The first clinical PCD-CT system allows a significant reduction in radiation exposure while maintaining image quality and image noise using a standardized non-contrast chest CT protocol.

Together We Go Farther: Improving Access to Cancer Screening Through a Multidisciplinary, One-Stop-Shop Approach.

RATIONALE AND OBJECTIVES: Despite significant scientific advances in cancer treatment in recent decades, Black Americans still face marked inequities in cancer screening, diagnosis, and treatment. Redressing these persistent inequities will require innovative strategies for community engagement. Radiologists, as experts in cancer screening and diagnosis for multiple malignancies, including breast, lung, and colon, are ideally suited to lead and implement community-based strategies to address local cancer disparities. MATERIALS AND METHODS: Through an established academic-community partnership in West Philadelphia built over the course of multiple prior community healthcare events, the authors piloted a novel radiology-led multidisciplinary approach to improve access to cancer screening for the predominantly Black, medically-underserved residents. Using a "one-stop-shop" framework to provide a comprehensive suite of screening and ancillary services in the heart of the community, the authors sought to remove as many impediments to screening as possible. RESULTS: Approximately 350 participants attended the health fair, and a total of 232 screening tests or assessments were completed. Data from this event suggest that this inclusive approach, as well as the use of a health fair "passport" to incentivize engagement, can successfully improve access to screening and follow-up in an underserved community. CONCLUSION: This "one-stop-shop" community approach can be replicated by radiology-led teams in other settings as a high-value, scalable opportunity to reduce disparities in access to cancer screening.

The Philadelphia Lung Cancer Learning Community: a multi-health-system, citywide approach to lung cancer screening.

BACKGROUND: Lung cancer screening uptake for individuals at high risk is generally low across the United States, and reporting of lung cancer screening practices and outcomes is often limited to single hospitals or institutions. We describe a citywide, multicenter analysis of individuals receiving lung cancer screening integrated with geospatial analyses of neighborhood-level lung cancer risk factors. METHODS: The Philadelphia Lung Cancer Learning Community consists of lung cancer screening clinicians and researchers at the 3 largest health systems in the city. This multidisciplinary, multi-institutional team identified a Philadelphia Lung Cancer Learning Community study cohort that included 11 222 Philadelphia residents who underwent low-dose computed tomography for lung cancer screening from 2014 to 2021 at a Philadelphia Lung Cancer Learning Community health-care system. Individual-level demographic and clinical data were obtained, and lung cancer screening participants were geocoded to their Philadelphia census tract of residence. Neighborhood characteristics were integrated with lung cancer screening counts to generate bivariate choropleth maps. RESULTS: The combined sample included 37.8% Black adults, 52.4% women, and 56.3% adults who currently smoke. Of 376 residential census tracts in Philadelphia, 358 (95.2%) included 5 or more individuals undergoing lung cancer screening, and the highest counts were geographically clustered around each health system's screening sites. A relatively low percentage of screened adults resided in census tracts with high tobacco retailer density or high smoking prevalence. CONCLUSIONS: The sociodemographic characteristics of lung cancer screening participants in Philadelphia varied by health system and neighborhood. These results suggest that a multicenter approach to lung cancer screening can identify vulnerable areas for future tailored approaches to improving lung cancer screening uptake. Future directions should use these findings to develop and test collaborative strategies to increase lung cancer screening at the community and regional levels.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa).

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

Cross-sectional review of US websites providing lung cancer screening recommendations following the 2021 US Preventive Services Task Force updates.

BACKGROUND: Lung cancer remains the leading cause of cancer death worldwide and an urgent public health priority. Early detection by low-dose CT (LDCT) screening and treatment of lung cancer has been shown to reduce mortality but uptake remains dismal, particularly among historically underserved groups. Following the US Preventive Services Task Force (USPSTF) expansion of its eligibility criteria to address inequities in utilization, efforts are needed to ensure dissemination of updated health information through digital means such as websites. OBJECTIVE: The objective of this study was to investigate whether online websites have been updated to reflect the recent USPSTF guidelines that expanded the recommended age and smoking pack-years for lung cancer screening. METHODS: In this cross-sectional study, we identified websites that provide information on lung cancer screening guidelines on May 24, 2022, approximately one year after the emergence of the updated USPSTF guidelines. The websites were assessed for recommended age to begin lung cancer screening and smoking pack-year quantity. RESULTS: Our study found that a lag in dissemination of updated lung cancer screening information exists. Approximately 1 year after the USPSTF guidelines were updated, 17-32% of websites providing information on lung cancer screening guidelines had not been updated. CONCLUSION: Routine monitoring of websites that provide information on lung cancer screening can help reduce misinformation, improve uptake of lung cancer screening, and prevent delays in diagnostic evaluation which disproportionally affects traditionally underserved populations.

Radiology for Thoracic Conditions in Low- and Middle-Income Countries.

With a disproportionately high burden of global morbidity and mortality caused by chronic respiratory diseases (CRDs) in low and middle-income countries (LMICs), access to radiological services is of critical importance for screening, diagnosis, and treatment guidance.

Lung-RADS Version 1.0 versus Lung-RADS Version 1.1: Comparison of Categories Using Nodules from the National Lung Screening Trial.

Background The American College of Radiology updated Lung Imaging Reporting and Data System (Lung-RADS) version 1.0 to version 1.1 in May 2019, with the two key changes involving perifissural nodules (PFNs) and ground-glass nodules (GGNs) now designated as a negative screening result. This study examines the effects of these changes using National Lung Screening Trial (NLST) data. Purpose To determine the frequency of PFNs and GGNs reclassified from category 3 or 4A to the more benign category 2 in the updated Lung-RADS version 1.1, as compared with Lung-RADS version 1.0, using CT scans from the NLST. Materials and Methods In this secondary analysis of the NLST, the authors studied all noncalcified nodules (NCNs) found on the incident scan. Nodules were evaluated using criteria from both Lung-RADS version 1.0 and version 1.1, which were compared to determine changes in the number of nodules deemed benign. A McNemar test was used to assess statistical significance. Results A total of 2813 patients (mean age ± standard deviation, 62 years ± 5; 1717 men) with 4408 NCNs were studied. Of the largest 1092 solid NCNs measuring at least 6 mm but less than 10 mm, 216 (19.8%) were deemed PFNs (category 2) using Lung-RADS version 1.1. Eleven of the 1092 solid NCNs (1.0%) were malignant, but none were PFNs. Of 161 GGNs, three (1.9%) were category 3 according to Lung-RADS version 1.0, of which two (66.7%) were down-classified to category 2 with version 1.1. One of the three down-categorized GGNs (version 1.1) proved to be malignant (false-negative finding). Statistically significant improvement for Lung-RADS version 1.1 was found for total nodules (P < .01) and PFNs (P < .01), but not GGNs (P = .48). Conclusion This secondary analysis of National Lung Screening Trial data shows that Lung Imaging Reporting and Data System version 1.1 decreased the number of false-positive results. This was related to the down-classification of perifissural nodules in the range of 6 up to 10 mm. The increase in allowable nodule size for ground-glass nodules in category 2 from 20 mm (version 1.0) to 30 mm (version 1.1) showed no benefit. © RSNA, 2021 See also the editorial by Mayo and Lam in this issue.

Adherence to annual lung cancer screening with low-dose CT scan in a diverse population.

PURPOSE: Our aim was to develop a novel approach for lung cancer screening among a diverse population that integrates the Centers for Medicare and Medicaid Services (CMS) recommended components including shared decision making (SDM), low-dose CT (LDCT), reporting of results in a standardized format, smoking cessation, and arrangement of follow-up care. METHODS: Between October of 2015 and March of 2018, we enrolled patients, gathered data on demographics, delivery of SDM, reporting of LDCT results using Lung-RADS, discussion of results, and smoking cessation counseling. We measured adherence to follow-up care, cancer diagnosis, cancer treatment, and smoking cessation at 2 years after initial LDCT. RESULTS: We enrolled 505 patients who were 57% African American, 30% Caucasian, 13% Hispanic, < 1% Asian, and 61% were active smokers. All participants participated in SDM, 88.1% used a decision aid, and 96.1% proceeded with LDCT. Of 496 completing LDCT, all received a discussion about results and follow-up recommendations. Overall, 12.9% had Lung-RADS 3 or 4, and 3.2% were diagnosed with lung cancer resulting in a false-positive rate of 10.7%. All 48 patients with positive screens but no cancer diagnosis adhered to follow-up care at 1 year, but only 35.4% adhered to recommended follow-up care at 2 years. The annual follow-up for patients with negative lung cancer screening results (Lung-RADS 1 and 2) was only 23.7% after one year and 2.8% after 2 years. All active smokers received smoking cessation counseling, but only 11% quit smoking. CONCLUSION: The findings show that an integrated lung cancer screening program can be safely implemented in a diverse population, but adherence to annual screening is poor.

Role of Imaging in Bronchoscopic Lung Volume Reduction Using Endobronchial Valve: State of the Art Review.

Chronic obstructive pulmonary disease (COPD) is becoming one of the leading causes of mortality and morbidity throughout the world. The National Emphysema Treatment Trial demonstrated that lung volume reduction surgery can improve pulmonary function, exercise capacity, and quality of life in select subgroups of patients with COPD. In recent years, few bronchoscopic lung volume reduction (BLVR) procedures have undergone clinical trials with the goal of establishing an effective and safe alternative approach for reducing hyperinflation in patients with severe emphysema who are symptomatic despite optimal medical management, but are poor surgical candidates. Of these BLVR procedures, only deployment of 1-way endobronchial valves (EBVs) has the largest pool of scientific data available to date to support its clinical utility. Two EBV systems have been food and drug administration-approved within the last year to meet the clinical demands of this select group of patients with COPD. On the basis of the results of multiple randomized clinical trials, the recommendations of the original 2016 Expert Panel Report on BLVR usage criteria of EBV have been updated in 2019. The outcome of EBV therapy is maximized in certain image-based COPD phenotypes. Imaging plays a major role in patient selection, target lobe identification, and in the management of postprocedural adverse events. With the expected widespread use of EBV therapy in the coming years, knowledge and familiarity of the Role of Imaging in BLVR using EBVs is essential for radiologists attempting to make meaningful contribution toward improving clinical outcomes.

Post-lung transplantation abdominopelvic complications: the role of multimodal imaging.

Lung transplantation (LT) is an established method for treating end-stage lung disease. Although most of the post-lung transplant imaging surveillance is focused on chronic lung allograft rejection, abdominopelvic complications have been reported in 7-62% of patients. The reported wide range of post-LT abdominopelvic complications is thought to be secondary to lack of current standardized definitions. These complications encompass a heterogeneous group of disorders including upper and lower gastrointestinal (GI) disorders, inflammatory conditions of solid organs, lymphoproliferative disorders, and neoplasms; each with varying pathophysiology, timing, severity, and treatment. Clinical manifestations of these complications may overlap or be masked by immunosuppression; therefore, imaging plays a paramount role in the early management and treatment.

High-resolution CT, histopathologic, and clinical features of granulomatous pneumocystis jiroveci pneumonia.

Although pneumocystis jiroveci pneumonia was historically associated with HIV/AID patients, there is a recent shift in demographics with increasing incidence in patients with hematologic malignancies and transplants. A granulomatous response to pneumocytis jiroveci infection is uncommon and most commonly presents as multiple randomly distributed nodules on chest imaging. Granulomatous pneumocytis jiroveci pneumonia presents with similar clinical manifestations as typical pneumocytis pneumonia but is usually not detected by bronchoalveolar lavage and may require biopsy for a definitive diagnosis. For this reason, the radiologist may be the first provider to suggest this diagnosis and guide management.

A simpler method for predicting weight loss in the first year after Roux-en-Y gastric bypass.

Factors postulated to predict weight loss after gastric bypass surgery, include race, age, gender, technique, height, and initial weight. This paper contained 1551 gastric bypass patients (85.9% female). Operations were performed by one surgeon (MLO) at community hospitals in Southern California from 1989 to 2008 with 314 being laparoscopic and 1237 open. We created the following equation: In[percent weight] = At(2) - Bt, where t was the time after operation (days) and A and B are constants. Analysis was completed on R-software. The model fits with R(2) value 0.93 and gives patients a realistic mean target weight with a confidence interval of 95% for the first year. Conclusion. We created a curve predicting weight loss after surgery as a percentage of initial weight. Initial weight was the single most important predictor of weight loss after surgery. Other recorded variables accounted for less than 1% of variability. Unknown factors account for the remaining 6-7%.