BackgroundEarly detection of colorectal cancer (CRC) is significantly associated with reduced morbidity and mortality. Virtual colonoscopy (VC) is a minimally invasive, safe and well-tolerated alternative procedure to traditional colonoscopy. Therefore, we aimed to evaluate the findings of VC particularly in supine and prone positions as well as to contribute to the practical challenges of procedure.MethodsTotal number of 20 patients who underwent VC were included in this retrospective study. After proper bowel cleansing was achieved, intestinal dilatation was performed by injecting air into the rectum. Two different shots were performed in the supine and prone positions. Additionally, intestinal diameters were measured from the cecum to the rectum at their widest point via 2-dimensional coronal reformat.ResultsPolyps were detected in 3 patients which were confirmed by optical colonoscopy. The mean cecum diameter was detected as the largest diameter in the supine and prone examinations. In both supine and prone examinations, the distal descending colon was the most challenging site. Additionally, the mean descending colon diameter calculated in the prone position (40.9 ± 6.4 mm) was found to be statistically larger than descending colon diameter calculated in the supine position (36.1 ± 5.3 mm) (P = 0.001).ConclusionsOur findings clearly demonstrated that combination of prone and supine scanning provides clear field of vision on narrow parts of the colon which improves accurate estimation for polyp detection. Furthermore, VC appears to be more comfortable, safe, fast, and cost-efffective procedure for CRC screening with advantages of low radiation exposure, extracolonic findings and lack of sedation requirements.

Colorectal cancer remains a leading cause of mortality worldwide, and early detection is essential for improving outcomes. CT colonography (CTC) has emerged as a promising alternative to optical colonoscopy for colorectal cancer screening. This article explores the potential of CTC in Japan, focusing on quality control, patient acceptability, complications, and its role in screening programs. CTC has demonstrated high sensitivity and specificity for detecting colorectal polyps, with its diagnostic performance comparable to colonoscopy for lesions ≥ 10 mm. Techniques such as fecal tagging and dual-position imaging significantly enhance diagnostic accuracy. However, the variability in diagnostic outcomes underscores the need for rigorous interpretation training and quality control. The American College of Radiology recommends training with at least 50 cases verified by colonoscopy. Despite its advantages, the adoption of CTC in Japan remains limited due to low awareness among medical professionals, a shortage of trained radiologists, and the absence of specific guidelines endorsing its use. Patient acceptability for CTC is high due to its non-invasive nature, shorter examination time, and reduced bowel preparation requirements compared to colonoscopy. Nonetheless, complications such as bowel perforation, albeit rare, necessitate careful risk assessment. While CTC has been recognized in the U.S. and Europe for screening and diagnostic follow-up, its integration into Japan's colorectal cancer screening guidelines is crucial to expand its utilization. To maximize the benefits of CTC, efforts must focus on standardizing methodologies, establishing quality indicators, and generating robust evidence on mortality reduction and cost-effectiveness.

This document serves to update the 2018 ACR Appropriateness Criteria® colorectal screening guidance document. In light of new recommendations from the US Preventative Services Task Force (USPSTF), an updated literature review of the imaging procedures for the screening of colorectal cancer was performed. Average-risk, elevated-risk, and high-risk individuals as well as those individuals who had an incomplete colonoscopy or were unable to tolerate colonoscopy were included. CT colonography without contrast was found to be usually appropriate for individuals at average and elevated risk between 45 to 75 years of age at initial screening. Additionally, CT colonography without contrast was found to be usually appropriate in individuals at average risk, elevated risk, and at high risk after incomplete colonoscopy or unable to tolerate colonoscopy. Other imaging procedures such as barium fluoroscopy and CT of the abdomen and pelvis were usually not appropriate. CT colonography without contrast, barium fluoroscopy, and CT of the abdomen and pelvis were usually not appropriate in high-risk individuals who can undergo a complete colonoscopy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Pelvic fractures are among the most complex challenges in orthopedic trauma, which usually involve hipbone and sacrum fractures, as well as joint dislocations. Traditional preoperative surgical planning relies on the operator's subjective interpretation of CT images, which is both time-consuming and prone to inaccuracies. This study introduces an automated preoperative planning solution for pelvic fracture reduction, addressing the limitations of conventional methods. The proposed solution includes a novel multi-scale distance-weighted neural network for segmenting pelvic fracture fragments from CT scans, and a learning-based approach to restore pelvic structure, combining a morphable model-based method for single-bone fracture reduction and a recursive pose estimation module for joint dislocation reduction. Comprehensive experiments on a clinical dataset of 30 fracture cases demonstrated the efficacy of our methods. Our segmentation network outperformed traditional max-flow segmentation and networks without distance weighting, achieving a Dice similarity coefficient (DSC) of 0.986 ± 0.055 and a local DSC of 0.940 ± 0.056 around the fracture sites. The proposed reduction method surpassed mirroring and mean template techniques, and an optimization-based joint matching method, achieving a target reduction error of (3.265 ± 1.485) mm, rotation errors of (3.476 ± 1.995)°, and translation errors of (2.773 ± 1.390) mm. In the proof-of-concept cadaver studies, our method achieved a DSC of 0.988 in segmentation and 3.731 mm error in reduction planning, which senior experts deemed excellent. In conclusion, our automated approach significantly improves traditional preoperative planning, enhancing both efficiency and accuracy in pelvic fracture reduction.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Most cases arise from polyps, which can be detected and removed before becoming cancerous. Computed tomography colonography (CTC), also known as virtual colonoscopy, was first introduced in 1994 as a minimally invasive method for CRC screening and diagnosis. This 2025 update on CTC will focus on (1) techniques and dose reduction strategies, (2) image display methods, (3) reporting and classification systems, (4) tumor staging capabilities, (5) integration of advanced imaging techniques, and (6) cost-effectiveness and reimbursement.

CT colonography (CTC) is a CT examination, performed with low dose and typically without IV contrast media, optimized to detect colorectal polyps and cancer. Despite extensive supporting data, CTC has had variable acceptance and use over the past two decades, particularly for a main indication of colorectal cancer screening. CTC is now at an inflection point after the approval in 2025 by CMS for reimbursement of CTC performed for colorectal cancer screening. Widespread use of CTC for CRC screening could help increase screening adherence rates and prevent cancer incidence. Nonetheless, radiologists must incorporate emerging knowledge regarding polyps' natural history and recognition of sessile serrated lesions, to leverage the screening efficiencies of CTC-based screening. The purposes of this article are to describe the current status of CRC in the United States and United Kingdom with consideration of historical reasons that have limited the test's use along with recent events that may portend a marked change in the test's acceptance; to highlight the challenges and potential solutions toward successful widespread CTC implementation; and to present new concepts in CTC and CRC screening relevant to radiologists.

Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligence (AI) algorithms have been employed for imaging diagnoses. In this study, we examined the sensitivity of neoplastic lesions in CT colonography images.

Lesion location and size were evaluated during colonoscopy and a large-scale database including a dataset for AI learning and external validation was created. The DICOM data used as training data and internal validation data (total 453 patients) for this study were colorectal cancer screening test data from two multicenter joint trial conducted in Japan and data from two institutions. External validation data (137 patients) were from other two institutions. Lesions were categorized into ≥6 mm, 6 to 10 mm, and ≥10 mm. During this study, we adopted a neural network structure that was designed based on the faster R-CNNs to detect colorectal lesion. The sensitivity of detecting colorectal lesions was verified when one and two positions were integrated.

Internal validation yielded sensitivity of 0.815, 0.738, and 0.883 for lesions ≥6 mm, 6 to 10 mm, and ≥10 mm, respectively, with a false lesion limit of three. Two external validation produced rates of 0.705 and 0.707, 0.575 and 0.573, and 0.760 and 0.779 for each lesion category. Combining two positions for each patient in calculating the sensitivity resulted in significantly improved rates for each lesion category.

The sensitivity of CT colonography images using the AI algorithm was improved by integrating evaluations in two positions. Validation experiments involving radiologists who can interpret images as well as AI to determine the auxiliary diagnosis can reduce the workload of physicians.

Computed tomographic colonography (CTC) is crucial for colorectal cancer screening and secondary examinations, often revealing extracolonic findings with unclear significance. This study retrospectively evaluated the impact of such findings on the prognosis and management of 103 patients (mean observation period [OP]: 2183 days), categorized according to the CT Colonography Reporting and Data System (C-RADS). The distributions were 17% (mean OP: 2,821 days) for E1, 38% for E2 (2,759 days), 6% for E3 (2,150 days), and 39% for E4 (1,338 days). Eighteen patients were further examined, but no treatment-related changes were observed in the E2 or E3 cases. Six of nine E4 patients responded to treatment (mean OP: 1,286 days). Kaplan-Meier analysis revealed worse prognosis for E4 (mean survival: 10.1 years) than for E1-E3 combined (5.6 years) (p < 0.0001). E4 findings are key in guiding treatment. The concordance rate between the clinical (Past E) and revised (Revised E) categories was high (0.83, 95% confidence interval: 0.77-0.88). The prognoses differed significantly between Revised E1-E3 (mean survival: 10.0 years) and Revised E4 (6.2 years) (p < 0.0001). Although E4 significantly affects prognosis, E2 and E3 had limited effects on treatment, highlighting the need for further study to improve diagnostic accuracy.

Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.

Five board-certified radiologists evaluated CT colonography images with colorectal polyps of all sizes and morphologies retrospectively and decided whether the depicted polyps required endoscopic resection. After a primary unassisted reading based on current guidelines, a second reading with access to the classification of a radiomics-based random-forest AI-model labelling each polyp as "non-adenomatous" or "adenomatous" was performed. Performance was evaluated using polyp histopathology as the reference standard.

77 polyps in 59 patients comprising 118 polyp image series (47% supine position, 53% prone position) were evaluated unassisted and AI-assisted by five independent board-certified radiologists, resulting in a total of 1180 readings (subsequent polypectomy: yes or no). AI-assisted readings had higher accuracy (76% +/- 1% vs. 84% +/- 1%), sensitivity (78% +/- 6% vs. 85% +/- 1%), and specificity (73% +/- 8% vs. 82% +/- 2%) in selecting polyps eligible for polypectomy (p < 0.001). Inter-reader agreement was improved in the AI-assisted readings (Fleiss' kappa 0.69 vs. 0.92).

AI-based characterisation of colorectal polyps at CT colonography as a second reader might enable a more precise selection of polyps eligible for subsequent endoscopic resection. However, further studies are needed to confirm this finding and histopathologic polyp evaluation is still mandatory.

Question This is the first study evaluating the impact of AI-based polyp classification in CT colonography on radiologists' therapy management. Findings Compared with unassisted reading, AI-assisted reading had higher accuracy, sensitivity, and specificity in selecting polyps eligible for polypectomy. Clinical relevance Integrating an AI tool for colorectal polyp classification in CT colonography could further improve radiologists' therapy recommendations.

Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.

The DL-SpiQA workflow involves auto-segmentation and labelling of all vertebral volumes on CT imaging using TotalSegmentator, an open-source deep learning algorithm based on nnU-Net, calculation of the radiation dose to each vertebra, and flagging and categorisation of potential treatments at the wrong anatomic level with automated email reports sent to involved radiation therapy personnel. We developed the DL-SpiQA tool based on retrospective clinical data from patients treated with palliative spine radiation therapy from sites included in the multicentre hospital network between Feb 12, 2014, and Nov 15, 2022. We used historic cases of patients who had a near-miss (ie, wrong-anatomic-level errors caught before the patient was treated) or had received wrong-anatomic-level treatment to test whether the tool could identify known errors successfully. We then used the tool prospectively over 15 months (April 24, 2023, to July 22, 2024) to evaluate any new spine radiation therapy treatment plan created for a patient, looking for any targeting errors, and dose and volume discrepancies. An email report was circulated with all the radiation therapy personnel; if any errors were found, these were highlighted and each error was defined. The tool was internally validated. All cases flagged by DL-SpiQA for both the retrospective and prospective studies were manually reviewed for dosimetric targeting, variant spine anatomy or spinal anomalies, and artificial intelligence (AI) segmentation errors. DL-SpiQA was further validated based on false positive and negative rates estimated from the retrospective results.

DL-SpiQA was first tested retrospectively on 513 patients with segmentation of 10 106 vertebrae. The system raised flags for ten dose discrepancies, 49 normal anatomic variants, 49 cases with implants or other anomalies, and 20 segmentation errors (4% false positive rate). DL-SpiQA caught one historic treatment at the wrong anatomic level and three near-misses. DL-SpiQA was then prospectively deployed, reviewing 520 cases and identifying six documentation errors, which triggered detailed review by clinicians, and 43 additional cases, which confirmed clinical knowledge of variant anatomy. In all detected cases (ie, 49 of 520 cases in total), the appropriate personnel were alerted. A false negative rate of 0·03% is estimated based on the 4% AI segmentation error rate and the frequency of reported spine radiation therapy errors.

The low false positive rate, the low false negative rate, and the high accuracy in flagging errors show that DL-SpiQA is an effective, AI-driven, automated quality assurance tool that could be used to identify anatomic spine variants and errors in targeting at the anatomic level. The tool could therefore help improve the safety of spine radiotherapy. Further external validation and tailoring is needed.

None.

Colonoscopy has been widely regarded as the gold standard for its high diagnostic accuracy and preventive potential. However, its invasive nature, high cost, and suboptimal participation rates limit its utility at the population level. Non-invasive screening tests, notably the fecal immunochemical test (FIT) and multitarget stool DNA tests, present promising alternatives that may improve screening participation and reduce barriers to participation. Among these, FIT has demonstrated a consistent advantage in enhancing participation, which subsequently contributes to better long-term outcomes in CRC prevention. FIT-based two-step screening offers several practical advantages, including cost-effectiveness, non-invasiveness, and greater flexibility. Moreover, the quantitative nature of FIT allows for adjustable sensitivity thresholds and the ability of risk stratification, making it adaptable across diverse populations and scenarios. Through serial testing, FIT can increase cumulative detection rates over time. This approach facilitates the identification of high-risk individuals, allowing for more judicious use of colonoscopy resources and reducing unnecessary invasive procedures, especially among low-risk populations. Notably, evidence indicates that participation to FIT-based screening is consistently higher than to colonoscopy, which enhances the detection of early-stage cancers and advanced adenomas in the long run. Given the constraints of limited endoscopic capacity, the aging population, and the recent lowering of the recommended screening age due to the rising incidence of early-onset CRC, FIT emerges as a practical, flexible solution. The role of two-step FIT screening in improving participation and enabling risk-stratified, personalized approaches to CRC prevention is pivotal, advocating for its expanded integration into future screening paradigms.

Although the association between appendicitis and colorectal cancer in older patients has received attention, postoperative colorectal screening through endoscopy is not currently recommended. This study conducted a systematic review of the literature on colorectal screening following appendectomy in adult patients.

A literature search was performed using online databases. Studies reporting colorectal surveillance after appendectomy in adult patients were retrieved for assessment.

Eight articles including a total of 3,995 patients were published between 2013 and 2023. An age of 40 years was the lower threshold in 6 of the 8 articles. Postoperative colorectal screening occurred in 771 patients (19.3%). Endoscopy was performed in 95.2% of cases and computed tomography-colonography in 4.8%. During endoscopic examinations, a lesion was discovered in 184 of 771 patients (24.0%), and an adenomatous polyp was found in 154 of 686 patients (22.5%). The overall cancer rate was 3.9% (30 of 771 patients). The tumor was located in the right-sided colon in 46.7% of the patients, in the cecum in 20.0%, in the rectum in 16.7%, in the left-sided colon in 10.0%, and in the sigmoid colon in 6.7%.

Performing post-appendectomy colorectal screening in patients >40 years of age could allow early detection of an underlying lesion.

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes.

Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

Imaging-based screening is an important public health focus and a fundamental part of Diagnostic Radiology. Hence, radiologists should be familiar with the concepts that drive imaging-based screening practice including goals, risks, biases and clinical trials. This review article discusses an array of imaging-based screening exams including the key epidemiology and evidence that drive screening guidelines for abdominal aortic aneurysm, breast cancer, carotid artery disease, colorectal cancer, coronary artery disease, lung cancer, osteoporosis, and thyroid cancer. We will provide an overview on societal interests in screening, screening-related inequities, and opportunities to address them. Emerging evidence for opportunistic screening and the role of AI in imaging-based screening will be explored. In-depth knowledge and formalized training in imaging-based screening strengthens radiologists as clinician scientists and has the potential to broaden our public health leadership opportunities. SUMMARY SENTENCE: An overview of key screening concepts, the evidence that drives today's imaging-based screening practices, and the need for radiologist leadership in screening policies and evidence development.

Colonoscopy is currently the best method for detecting bowel cancer, but fundamental design and construction have not changed significantly in decades. Conventional colonoscope (CC) is difficult to maneuver and can lead to pain with a risk of damaging the bowel due to its rigidity. We present the MorphGI, a robotic endoscope system that is self-propelling and made of soft material, thus easy to operate and inherently safe to patient. After verifying kinematic control of the distal bending segment, the system was evaluated in: a benchtop colon simulator, using multiple colon configurations; a colon simulator with force sensors; and surgically removed pig colon tissue. In the colon simulator, the MorphGI completed a colonoscopy in an average of 10.84 min. The MorphGI showed an average of 77% and 62% reduction in peak forces compared to a CC in high- and low-stiffness modes, respectively. Self-propulsion was demonstrated in the excised tissue test but not in the live pig test, due to anatomical differences between pig and human colons. This work demonstrates the core features of MorphGI.

Colorectal cancer (CRC) has the highest mortality rate among men and is the second highest among women under fifty, with incidence and mortality rates rising in younger populations. Studies indicate that up to one-third of patients diagnosed before fifty have a family history or genetic factors, highlighting the need for earlier screening. Contrariwise, diagnosis in healthy subjects through screening strategies enables early-stage detection of the tumor and better clinical outcomes. In recent years, mortality rates of CRC in Western countries have been on a steady decline, which is largely attributed to widespread screening programs and advancements in treatment modalities. Indeed, early detection through screening significantly improves prognosis, with stark differences in survival rates between localized and metastatic disease. This article aims to provide a comprehensive review of the existing literature, delving into the performance and efficacy of various CRC screening strategies. It navigates through available screening tools, evaluating their efficacy and cost-effectiveness. The discussion extends to delineating target populations for screening, emphasizing the importance of tailored approaches for individuals at heightened risk.

The femoral neck is the most vulnerable site for fractures within the hip joint. Due to its complex three-dimensional structure and special blood supply mechanism, the treatment of femoral neck fractures is difficult and the healing probability is low. Using computers to accurately and automatically locate the femoral neck axis can detect the density of femoral neck, the neck-shaft angle and the anteversion angle, which effectively assists in the prevention and treatment of femoral neck fractures. Additionally, the traditional femoral neck axis positioning schemes have limitations in accuracy, automation and assistance to bone density measurement. Therefore, this paper proposes a new fully automatic femoral neck axis positioning method. First, the coronal plane's three-dimensional reconstruction highlights the details of the target bone, and then designs a coarse localization module based on multi-scale template matching to obtain the rough range of the femoral neck axis. Then, a detailed localization module based on the femoral neck virtual slices is used to obtain the contour centers and accurately locates the three-dimensional femoral neck axis. This method has been validated in comparison with the manual measurement method. Experimental results revealed that the extracted femoral neck axis in this study can achieve automation, ensure accuracy, and avoid subjective effects effectively and has the potential value to be applied in the prevention and treatment of femoral neck fractures.

Colorectal cancer (CRC) may develop from focal changes within benign or precancerous polyps. The immune system's failure to detect and eradicate tumor cells due to immune surveillance evasion, allows cancer to develop and spread. This study aims to analyze the differences in circulating lymphocyte subpopulations in patients with colorectal inflammatory polyps, colorectal adenomas and CRC.

We retrospectively reviewed patients from September 2016 to December 2019 at the Shaoxing Second Hospital. Using flow cytometry, the subset distribution and immunophenotype of T cells, CD4+ T cells, CD8+ T cells, B cells and NK cells were investigated in peripheral blood mononuclear cell samples. The counts of lymphocytes were determined by white blood cell counts.

In total, 518 patients were included in this study. The counts of lymphocytes, T cells and NK cells in patients with inflammatory polyps, colorectal adenomas and CRC were lower than controls. The counts and percentages of CD8+ T cells in patients with inflammatory polyps, colorectal adenomas and CRC were lower than controls. The counts of CD4+ T cells were lower in patients with CRC than inflammatory polyps. The percentages of CD4+ T cells in patients with inflammatory polyps, colorectal adenomas and CRC were higher than controls, but lower in the CRC than inflammatory polyps, colorectal adenomas. The counts and percentages of B cells were lower in CRC patients than colorectal adenomas patients. In addition, the percentages of B cells were higher in patients with inflammatory polyps and colorectal adenomas than in controls.

The decrease in counts of lymphocyte, T cells, CD8+ T cells and NK cells in patients may be related to the dysplasia of epithelial cells. Furthermore, the B cells and CD4+ T cells may be related to the malignant growth of the dysplastic epithelial cells.

Colon cancer is the third most common malignancy in Canada. Computed tomography colonography (CTC) provides a creditable and validated option for colon screening and assessment of known pathology in patients for whom conventional colonoscopy is contraindicated or where patients self-select to use imaging as their primary modality for initial colonic assessment. This updated guideline aims to provide a toolkit for both experienced imagers (and technologists) and for those considering launching this examination in their practice. There is guidance for reporting, optimal exam preparation, tips for problem solving to attain high quality examinations in challenging scenarios as well as suggestions for ongoing maintenance of competence. We also provide insight into the role of artificial intelligence and the utility of CTC in tumour staging of colorectal cancer. The appendices provide more detailed guidance into bowel preparation and reporting templates as well as useful information on polyp stratification and management strategies. Reading this guideline should equip the reader with the knowledge base to perform colonography but also provide an unbiased overview of its role in colon screening compared with other screening options.

Colorectal cancer (CRC) is a leading cause of cancer incidence and mortality. Screening can result in reductions in incidence and mortality, but there are many challenges to uptake and follow-up.

Here, we will review the changing epidemiology of CRC, including increasing trends for early and later onset CRC; evidence to support current and emerging screening strategies, including noninvasive stool and blood-based tests; key challenges to ensuring uptake and high-quality screening; and the critical role that clinical laboratories can have in supporting health system and public health efforts to reduce the burden of CRC on the population.

Clinical laboratories have the opportunity to play a seminal role in optimizing early detection and prevention of CRC.

Pelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the accuracies of bone segmentation and landmark detection, leading to improper surgery planning and potential operative complications.

This work proposes a two-stage multi-task algorithm to improve the accuracy of pelvic bone segmentation and landmark detection, especially for the diseased cases. The two-stage framework uses a coarse-to-fine strategy which first conducts global-scale bone segmentation and landmark detection and then focuses on the important local region to further refine the accuracy. For the global stage, a dual-task network is designed to share the common features between the segmentation and detection tasks, so that the two tasks mutually reinforce each other's performance. For the local-scale segmentation, an edge-enhanced dual-task network is designed for simultaneous bone segmentation and edge detection, leading to the more accurate delineation of the acetabulum boundary.

This method was evaluated via threefold cross-validation based on 81 CT images (including 31 diseased and 50 healthy cases). The first stage achieved DSC scores of 0.94, 0.97, and 0.97 for the sacrum, left and right hips, respectively, and an average distance error of 3.24 mm for the bone landmarks. The second stage further improved the DSC of the acetabulum by 5.42%, and this accuracy outperforms the state-of-the-arts (SOTA) methods by 0.63%. Our method also accurately segmented the diseased acetabulum boundaries. The entire workflow took ~ 10 s, which was only half of the U-Net run time.

Using the multi-task networks and the coarse-to-fine strategy, this method achieved more accurate bone segmentation and landmark detection than the SOTA method, especially for diseased hip images. Our work contributes to accurate and rapid design of acetabular cup prostheses.

Studies of tin spectral filtration have demonstrated potential in reducing radiation dose while maintaining image quality for unenhanced computed tomography (CT) scans. The extent of dose reduction, however, was commonly measured using the change in the scanner's reported CTDIvol . This method does not account for how tin filtration affects patient organ and effective dose.

To investigate the effect of tin filtration on patient organ and effective dose for CT Lung Cancer Screening (LCS) and CT Colonography (CTC).

A previously-developed Monte Carlo program was adapted to model a 96-row CT scanner (Somatom Force, Siemens Healthineers) with tin filtration capabilities at 100 kV (100Sn) and 150 kV (150Sn). The program was then validated using experimental CTDIvol measurements at all available kV (70-150 kV) and tin-filtered kV options (100Sn and 150Sn). After validation, the program simulated LCS scans of the chest and CTC scan of the abdomen-pelvis for a population of 53 computational patient models from the extended cardiac-torso family. Each scan was performed using three different spectra: 120 kV, 100Sn, and 150Sn. CTDIvol -normalized organ doses and DLP-normalized effective doses, commonly referred to as dose conversion factors, were compared between the different spectra.

For all LCS and CTC scans, CTDIvol -normalized organ doses and DLP-normalized effective doses increased with increasing beam hardness (120 kV, 100Sn, 150 Sn). For LCS, relative for 120 kV, conversion factors for 100Sn produced a median increase in effective dose of 9%, with organ dose increases of 8% to lung, 5% to breast, 15% to thyroid, and 3% to skin. Conversion factors for 150Sn produced a median increase in effective dose of 20%, with organ dose increases of 16%, 18%, 26%, and 12% to these same organs, respectively. For CTC, relative for 120 kV, conversion factors for 100Sn produced a median increase in effective dose of 12%, with organ dose increases of 9% to colon, 10% to liver, 11% to stomach, and 4% to skin. Conversion factors for 150Sn produced a median increase in effective dose of 21%, with organ dose increases of 16%, 17%, 19%, and 10% to these same organs, respectively.

Results show that dose conversion factors are greater when using tin filtration and should be considered when evaluating tin's potential for dose reduction.

Intraoperative accurate localization of tumors in the lower gastrointestinal tract is essential to ensure oncologic radicality. In minimally invasive colon surgery, tactile identification of tumors is challenging due to diminished or absent haptics. In clinical practice, preoperative endoscopic application of a blue dye (ink) to the tumor site has become the standard for marking and identification of tumors in the colon. However, this method has the major limitation that accidental intraperitoneal spillage of the dye can significantly complicate the identification of anatomical structures and surgical planes. In this work, we describe a new approach of NIR fluorescent tattooing using a near-infrared (NIR) fluorescent marker instead of a blue dye (ink) for endoscopic tattooing.

AFS81x is a newly developed NIR fluorescent marker. In an experimental study with four domestic pigs, the newly developed NIR fluorescent marker (AFS81x) was used for endoscopic tattooing of the colon. 7-12 endoscopic submucosal injections of AFS81x were placed per animal in the colon. On day 0, day 1, and day 10 after endoscopic tattooing with AFS81x, the visualization of the fluorescent markings in the colon was evaluated during laparoscopic surgery by two surgeons and photographically documented.

The detection rate of the NIR fluorescent tattoos at day 0, day 1, and day 10 after endoscopic tattooing was 100%. Recognizability of anatomical structures during laparoscopy was not affected in any of the markings, as the markings were not visible in the white light channel of the laparoscope, but only in the NIR channel or in the overlay of the white light and the NIR channel of the laparoscope. The brightness, the sharpness, and size of the endoscopic tattoos did not change significantly on day 1 and day 10, but remained almost identical compared to day 0.

The new approach of endoscopic NIR fluorescence tattooing using the newly developed NIR fluorescence marker AFS81x enables stable marking of colonic sites over a long period of at least 10 days without compromising the recognizability of anatomical structures and surgical planes in any way.

Patient-centered care (PCC) and equity are two of the six core domains of quality health care, according to the Institute of Medicine. Exceptional imaging care requires radiology practices to provide patient-centered (i.e., respectful and responsive to individual patient preferences, needs, and values) and equitable (i.e., does not vary in quality on the basis of gender, ethnicity, geographic location, or socioeconomic status) care. Specific barriers that prevent the delivery of patient-centered equitable care include information gaps, breaches of trust, organizational medical culture, and financial incentives. Information gaps limit practitioners in understanding the lived experience of patients. Breaches of trust prevent patients from seeking needed medical care. Organizational medical cultures may not be centered around patient experiences. Financial incentives can impede practitioners' ability to spend the time and resources required to meet patient goals and needs. Intentional approaches that integrate core principles in both PCC and health equity are required to deliver high-quality patient-centered imaging care for diverse patient populations. The purpose of this AJR Expert Panel Narrative Review is to review the origins of the PCC movement in radiology, characterize connections between the PCC and health equity movements, and describe concrete examples of ways to foster patient-centered equitable care in radiology.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Since the early 2000s, there has been a rapid decline in colorectal cancer (CRC) mortality, due in large part to screening and removal of precancerous polyps. Despite these improvements, CRC remains the second leading cause of cancer deaths in the United States, with approximately 53,000 deaths projected in 2023. The aim of this American Gastroenterological Association (AGA) Clinical Practice Update Expert Review was to describe how individuals should be risk-stratified for CRC screening and post-polypectomy surveillance and to highlight opportunities for future research to fill gaps in the existing literature.

This Expert Review was commissioned and approved by the American Gastroenterological Association (AGA) Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership, and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. These Best Practice Advice statements were drawn from a review of the published literature and from expert opinion. Because systematic reviews were not performed, these Best Practice Advice statements do not carry formal ratings regarding the quality of evidence or strength of the presented considerations. Best Practice Advice Statements BEST PRACTICE ADVICE 1: All individuals with a first-degree relative (defined as a parent, sibling, or child) who was diagnosed with CRC, particularly before the age of 50 years, should be considered at increased risk for CRC. BEST PRACTICE ADVICE 2: All individuals without a personal history of CRC, inflammatory bowel disease, hereditary CRC syndromes, other CRC predisposing conditions, or a family history of CRC should be considered at average risk for CRC. BEST PRACTICE ADVICE 3: Individuals at average risk for CRC should initiate screening at age 45 years and individuals at increased risk for CRC due to having a first-degree relative with CRC should initiate screening 10 years before the age at diagnosis of the youngest affected relative or age 40 years, whichever is earlier. BEST PRACTICE ADVICE 4: Risk stratification for initiation of CRC screening should be based on an individual's age, a known or suspected predisposing hereditary CRC syndrome, and/or a family history of CRC. BEST PRACTICE ADVICE 5: The decision to continue CRC screening in individuals older than 75 years should be individualized, based on an assessment of risks, benefits, screening history, and comorbidities. BEST PRACTICE ADVICE 6: Screening options for individuals at average risk for CRC should include colonoscopy, fecal immunochemical test, flexible sigmoidoscopy plus fecal immunochemical test, multitarget stool DNA fecal immunochemical test, and computed tomography colonography, based on availability and individual preference. BEST PRACTICE ADVICE 7: Colonoscopy should be the screening strategy used for individuals at increased CRC risk. BEST PRACTICE ADVICE 8: The decision to continue post-polypectomy surveillance for individuals older than 75 years should be individualized, based on an assessment of risks, benefits, and comorbidities. BEST PRACTICE ADVICE 9: Risk-stratification tools for CRC screening and post-polypectomy surveillance that emerge from research should be examined for real-world effectiveness and cost-effectiveness in diverse populations (eg, by race, ethnicity, sex, and other sociodemographic factors associated with disparities in CRC outcomes) before widespread implementation.

To examine the use of CT, emergency department (ED)-presentation and hospitalisation and in 12 months before and after a diagnosis of cancer.

Population-based retrospective cohort study.

West Australian linked administrative records at individual level.

104 009 adults newly diagnosed with cancer in 2004-2014.

CT use, ED presentations, hospitalisations.

As compared with the rates in the 12th month before diagnosis, the rate of CT scans started to increase from 2 months before diagnosis with an increase in both ED presentations and hospitalisation from 1 month before the diagnosis. These rates peaked in the month of diagnosis for CT scans (477 (95% CI 471 to 482) per 1000 patients), and for hospitalisations (910 (95% CI 902 to 919) per 1000 patients), and the month prior to diagnosis for ED (181 (95% CI 178 to 184) per 1000 patients) then rapidly reduced after diagnosis but remained high for the next 12 months. While the patterns of the health services used were similar between 2004 and 2014, the rate of the health services used during after diagnosis was higher in 2014 versus 2004 except for CT use in patients with lymphohaematopoietic cancer with a significant reduction.

Our results showed an increase in demand for health services from 2 months before diagnosis of cancer. Increasing use of health services during and post cancer diagnosis may warrant further investigation to identify factors driving this change.

Colorectal cancer (CRC) is a leading cause of morbidity and mortality worldwide and its incidence increases with age. The proportion of older adults in the United States continues to rise, making CRC prevention a key health priority for our aging population. CRC is a largely preventable disease through screening and polyp surveillance, and noninvasive modalities represent an important option for older adults in whom the burdens and risks of invasive testing are higher compared with younger adults. This review highlights the evidence, risks, and benefits of noninvasive CRC screening and surveillance options in older adults and discusses the challenges of CRC prevention in this cohort.

The purpose of this updated guidance statement is to guide clinicians on screening for colorectal cancer (CRC) in asymptomatic average-risk adults. The intended audience is all clinicians. The population is asymptomatic adults at average risk for CRC.

This updated guidance statement was developed using recently published and critically appraised clinical guidelines from national guideline developers since the publication of the American College of Physicians' 2019 guidance statement, "Screening for Colorectal Cancer in Asymptomatic Average-Risk Adults." The authors searched for national guidelines from the United States and other countries published in English using PubMed and the Guidelines International Network library from 1 January 2018 to 24 April 2023. The authors also searched for updates of guidelines included in the first version of our guidance statement. The Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument was used to assess the quality of eligible guidelines. Two guidelines were selected for adoption and adaptation by raters on the basis of the highest average overall AGREE II quality scores. The evidence reviews and modeling studies for these 2 guidelines were also used to synthesize the evidence of diagnostic test accuracy, effectiveness, and harms of CRC screening interventions and to develop our guidance statements.

Clinicians should start screening for colorectal cancer in asymptomatic average-risk adults at age 50 years.

Clinicians should consider not screening asymptomatic average-risk adults between the ages of 45 to 49 years. Clinicians should discuss the uncertainty around benefits and harms of screening in this population.

Clinicians should stop screening for colorectal cancer in asymptomatic average-risk adults older than 75 years or in asymptomatic average-risk adults with a life expectancy of 10 years or less.

Clinicians should select a screening test for colorectal cancer in consultation with their patient based on a discussion of benefits, harms, costs, availability, frequency, and patient values and preferences.

Clinicians should select among a fecal immunochemical or high-sensitivity guaiac fecal occult blood test every 2 years, colonoscopy every 10 years, or flexible sigmoidoscopy every 10 years plus a fecal immunochemical test every 2 years as a screening test for colorectal cancer.

Clinicians should not use stool DNA, computed tomography colonography, capsule endoscopy, urine, or serum screening tests for colorectal cancer.

Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide. Early detection of this neoplasia has proven to improve prognosis, resulting in a 90% increase in survival. However, available CRC screening methods have limitations, requiring the development of new tools. MicroRNA biomarkers have emerged as a powerful screening tool, as they are highly expressed in CRC patients and easily detectable in several biological samples. While microRNAs are extensively studied in blood samples, recent interest has now arisen in other samples, such as stool samples, where they can be combined with existing screening methods. Among the microRNAs described in the literature, microRNA-21-5p and microRNA-92a-3p and their cluster have demonstrated high potential for early CRC screening. Furthermore, the combination of multiple microRNAs has shown improved performance in CRC detection compared to individual microRNAs. This review aims to assess the available data in the literature on microRNAs as promising biomarkers for early CRC screening, explore their advantages and disadvantages, and discuss the optimal study characteristics for analyzing these biomarkers.

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths despite being the most preventable and treatable forms of cancer when caught early through screening. There is an unmet need for novel screening approaches with improved accuracy, less invasiveness, and reduced costs. In recent years, evidence has accumulated around particular biological events that happen during the adenoma-to-carcinoma transition, especially focusing on precancerous immune responses in the colonic crypt. Protein glycosylation plays a central role in driving those responses, and recently, numerous reports have been published on how aberrant protein glycosylation both in colonic tissue and on circulating glycoproteins reflects these precancerous developments. The complex field of glycosylation, which exceeds complexity of proteins by several orders of magnitude, can now be studied primarily because of the availability of new high-throughput technologies such as mass spectrometry and artificial intelligence-powered data processing. This has now opened new avenues for studying novel biomarkers for CRC screening. This review summarizes the early events taking place from the normal colon mucosa toward adenoma and adenocarcinoma formation and associated critical protein glycosylation phenomena, both on the tissue level and in the circulation. These insights will help establish an understanding in the interpretation of novel CRC detection modalities that involve high-throughput glycomics.

Dedicated radiological expertise and a high-quality examination, performed according to current technical standards and for accepted indications, are prerequisite to achieve excellent results with CT colonography (CTC).

The aim of this article is to review current standards of the examination technique as well as indications and contraindications for CTC based on recent recommendations and guidelines.

Based on extensive literature research, current knowledge about the examination technique and the indications and contraindications is summarized.

CTC is the radiological examination of choice for the detection of colorectal neoplasia. Beside incomplete or refused colonoscopy and contraindications to colonoscopy, CTC is also a noninvasive option for opportunistic colorectal cancer screening. The examination technique is based on a CTC-specific patient preparation scheme that includes fecal tagging, colonic distension, low-dose CT scans in two patient positions and a combined 2D and 3D data evaluation.

Performing CTC according to current technical standards is prerequisite for high-quality examinations and is, thus, also a key factor to obtain a correct diagnosis. CTC is a noninvasive examination, capable of providing clinically relevant diagnoses for a wide range of indications.

The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. Next, we assessed the DL models on the NVIDIA GeForce RTX 3080 Laptop GPU (16GB GDDR6 VRAM) using 5400 processed images divided equally between normal colons and colons with cancer for each of the imaging modalities used. Comparing the imaging modalities when applied to the five DL models presented in this study and twenty-six ensemble DL models, the experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model under the TL setting, outperforms all the other models by generating the highest average performance result of 99.1% (99.1%, 99.8%, and 99.1%) based on the accuracy results (AUC, precision, and F1, respectively).

To determine the level of heterogeneity in delivery of computed tomography (CT) colonography services and develop a workforce calculator that accommodates the variation identified.

A national survey, based on the "WHO workforce indicators of staffing need", established activity standards for essential tasks in delivery of the service. From these data a workforce calculator was designed to guide the required staffing and equipment resource by service size.

Activity standards were established as mode responses >70%. Service homogeneity was greater in areas where professional standards and guidance were available. The mean service size was 1,101. Did not attend (DNA) rates were lower where direct booking was available (p<0.0001). Service sizes were larger where radiographer reporting was embedded in reporting paradigms (p<0.024).

The survey identified benefits of radiographer-led direct booking and reporting. The workforce calculator derived from the survey provides a framework to guide the resourcing of expansion while maintaining standards.

The low rates of colonic malignancy detected on interval colonoscopy for patients diagnosed with diverticulitis have led recent studies to question the utility of the practice. The aim of this study was to assess the detection rate of colorectal cancer on colonoscopy for patients with a first episode of acute uncomplicated diverticulitis across three separate centres in Ireland and the UK.

A retrospective review was performed of patients with a first episode of acute, uncomplicated diverticulitis who underwent interval colonoscopy at three separate centres in the UK and Ireland between 2007 and 2019. The follow-up period was one year.

A total of 5485 patients were admitted with acute diverticulitis between the three centres. All patients had CT verified diverticulitis. A 90.8% (n = 4982) underwent subsequent colonic evaluation with colonoscopy. Of these, a histologically proven diagnosis of colorectal carcinoma was made in 1.28% (n = 64).

Routine colonoscopy following an episode of acute, uncomplicated diverticulitis may not be necessary in every patient. It may be appropriate to reserve this more invasive investigation for those with higher risk factors for malignancy.

There are no robust tools for the diagnosis of synchronous colorectal cancer (SyCRC). Herein, we developed the first methylation signature to identify and characterise patients with SyCRC.

For biomarker discovery, we analysed the genome-wide methylation profiles of 16 SyCRC and 18 solitary colorectal cancer (SoCRC) specimens. We thereafter established a methylation signature risk-scoring model to identify SyCRC in an independent cohort of 38 SyCRC and 42 SoCRC patients. In addition, we evaluated the prognostic value of the identified methylation profile.

We identified six differentially methylated CpG probes/sites that distinguished SyCRC from SoCRC. In the validation cohort, we developed a methylation panel that identified patients with SyCRC from not only larger tumour (AUC = 0.91) but also the paired remaining tumour (AUC = 0.93). Moreover, high risk scores of our panel were associated with the development of metachronous CRC among patients with SyCRC (AUC = 0.87) and emerged as an independent predictor for relapse-free survival (hazard ratio = 2.72; 95% CI = 1.12-6.61). Furthermore, the risk stratification model which combined with clinical risk factors was a diagnostic predictor of recurrence (AUC = 0.90).

Our novel six-gene methylation panel robustly identifies patients with SyCRC, which has the clinical potential to improve the diagnosis and management of patients with CRC.

Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides "filet"-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.

population-based finite element analysis of hip joints allows us to understand the effect of inter-subject variability on simulation results. Developing large subject-specific population models is challenging and requires extensive manual effort. Thus, the anatomical representations are often subjected to simplification. The discretized geometries do not guarantee conformity in shared interfaces, leading to complications in setting up simulations. Additionally, these models are not openly accessible, challenging reproducibility. Our work provides multiple subject-specific hip joint finite element models and a novel semi-automated modeling workflow.

we reconstruct 11 healthy subject-specific models, including the sacrum, the paired pelvic bones, the paired proximal femurs, the paired hip joints, the paired sacroiliac joints, and the pubic symphysis. The bones are derived from CT scans, and the cartilages are generated from the bone geometries. We generate the whole complex's volume mesh with conforming interfaces. Our models are evaluated using both mesh quality metrics and simulation experiments.

the geometry of all the models are inspected by our clinical expert and show high-quality discretization with accurate geometries. The simulations produce smooth stress patterns, and the variance among the subjects highlights the effect of inter-subject variability and asymmetry in the predicted results.

our work is one of the largest model repositories with respect to the number of subjects and regions of interest in the hip joint area. Our detailed research data, including the clinical images, the segmentation label maps, the finite element models, and software tools, are openly accessible on GitHub and the link is provided in Moshfeghifar et al.(2022)[1]. Our aim is to empower clinical researchers to have free access to verified and reproducible models. In future work, we aim to add additional structures to our models.

Colorectal cancer screening is recommended for people aged 50-75 years, but the optimal screening test and strategy are not established. We aimed to compare single CT colonography versus three faecal immunochemical test (FIT) rounds for population-based screening of colorectal cancer.

This randomised controlled trial was done in Florence, Italy. Adults aged 54-65 years, never screened for colorectal cancer, were randomly assigned (1:2) by simple randomisation and invited by post to either a single CT colonography (CT colonography group) or three FIT rounds (FIT group; each round was done 2 years apart). Exclusion criteria included previous colorectal cancer, advanced adenoma, or inflammatory bowel disease, colonoscopy within the last 5 years or FIT within the last 2 years, and severe medical conditions. Participants who had a colonic mass or at least one polyp of 6 mm or more in diameter in the CT colonography group and those who had at least 20 μg haemoglobin per g faeces in the FIT group were referred for work-up optical colonoscopy. The primary outcome was detection rate for advanced neoplasia. Outcomes were assessed in the modified intention-to-screen and per-protocol populations. The trial is registered with ClinicalTrials.gov, NCT01651624.

From Dec 12, 2012, to March 5, 2018, 14 981 adults were randomised and invited to screening interventions. 5242 (35·0%) individuals (2809 [53·6%] women and 2433 [46·4%] men) were assigned to the CT colonography group and 9739 (65·0%) individuals (5208 [53·5%] women and 4531 [46·5%] men) were assigned to the FIT group. Participation in the screening intervention was lower in the CT colonography group (1286 [26·7%] of the 4825 eligible invitees) than it was for the FIT group (6027 [64·9%] of the 9288 eligible invitees took part in at least one screening round, 4573 [49·2%] in at least two rounds, and 3105 [33·4%] in all three rounds). The detection rate for advanced neoplasia of CT colonography was significantly lower than the detection rate after three FIT rounds (1·4% [95% CI 1·1-1·8] vs 2·0% [1·7-2·3]; p=0·0094) in the modified intention-to-screen analysis, but the detection rate was significantly higher in the CT colonography group than in the FIT group (5·2% [95% CI 4·1-6·6] vs 3·1% [2·7-3·6]; p=0·0002]) in the per-protocol analysis. Referral rate to work-up optical colonoscopy (the secondary outcome of the trial) was significantly lower for the CT colonography group than for the FIT group after three FIT rounds (2·7% [95% CI 2·2-3·1] vs 7·5% [7·0-8·1]; p<0·0001) in the modified intention-to-screen analysis, whereas no significant difference was observed in the per-protocol analysis (10·0% [8·4-11·8] vs 11·6% [10·8-12·4]). No major complications were observed in the CT colonography group after screening and work-up optical colonoscopy, whereas three cases of bleeding were reported in the FIT group after work-up optical colonoscopy (two after the first FIT and one after the second FIT).

Greater participation makes FIT more efficient than single CT colonography for detection of advanced neoplasia in population screening for colorectal cancer. Nonetheless, higher detection rate in participants and fewer work-up colonoscopies are possible advantages of CT colonography as a screening tool, which might deserve consideration in future trials.

Government of Tuscany and Cassa di Risparmio di Firenze Foundation.

For the Italian translation of the abstract see Supplementary Materials section.

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.

Colorectal polyps are removed to prevent progression to colorectal cancer. Polyp size is an important factor for risk stratification of malignant transformation. Endoscopic size estimation correlates poorly with pathological reports and several factors have been suggested to influence size estimation. We aimed to gain insight into the factors influencing endoscopic polyp size estimation.

Images of polyps in an artificial model were obtained at 1, 3, and 5 cm from the colonoscope's tip. Participants were asked to estimate the diameter and volume of each polyp.

Fifteen endoscopists from three large-volume centers participated in this study. With an intraclass correlation coefficient of 0.66 (95% confidence interval [CI], 0.62-0.71) for diameter and 0.56 (95% CI, 0.50-0.62) for volume. Polyp size estimated at 3 cm from the colonoscope's tip yielded the best results. A lower distance between the tip and the polyp was associated with a larger estimated polyp size.

Correct endoscopic estimation of polyp size remains challenging. This finding can affect size estimation skills and future training programs for endoscopists.