Radiologic Predictors of Increased Number of Necrosectomies During Endoscopic Management of Walled-off Pancreatic Necrosis.

GOALS: No established methods exist to predict who will require a higher number of endoscopic necrosectomy sessions for walled-off necrosis (WON). We aim to identify radiologic predictors for requiring a greater number of necrosectomy sessions. This may help to identify patients who benefit from aggressive endoscopic management. MATERIALS AND METHODS: This is a multicenter retrospective study of patients with WON at 3 tertiary care centers. WON characteristics on preintervention computed tomography imaging were evaluated to determine if they were predictive of requiring more endoscopic necrosectomy. RESULTS: A total of 104 patients were included. Seventy patients (67.3%) underwent endoscopic necrosectomy, with median of 2 necrosectomies. WON largest transverse diameters (P=0.02), largest coronal diameters (P=0.01), necrosis pattern [likelihood ratio (LR)=17.85, P<0.001], spread (LR=11.02, P=0.01), hemorrhage (LR=8.64, P=0.003), and presence of disconnected pancreatic duct (LR=6.80, P=0.01) were associated with undergoing ≥2 necrosectomies. Patients with septations/loculations were significantly less likely to undergo ≥2 necrosectomies (LR=4.86, P=0.03). CONCLUSIONS: Several computed tomography radiologic features were significantly associated with undergoing ≥2 necrosectomies. These could help identify patients who will undergo a higher number of endoscopic necrosectomy sessions.

Peer evaluation and feedback for invasive medical procedures: a systematic review.

BACKGROUND: There is significant variability in the performance and outcomes of invasive medical procedures such as percutaneous coronary intervention, endoscopy, and bronchoscopy. Peer evaluation is a common mechanism for assessment of clinician performance and care quality, and may be ideally suited for the evaluation of medical procedures. We therefore sought to perform a systematic review to identify and characterize peer evaluation tools for practicing clinicians, assess evidence supporting the validity of peer evaluation, and describe best practices of peer evaluation programs across multiple invasive medical procedures. METHODS: A systematic search of Medline and Embase (through September 7, 2021) was conducted to identify studies of peer evaluation and feedback relating to procedures in the field of internal medicine and related subspecialties. The methodological quality of the studies was assessed. Data were extracted on peer evaluation methods, feedback structures, and the validity and reproducibility of peer evaluations, including inter-observer agreement and associations with other quality measures when available. RESULTS: Of 2,135 retrieved references, 32 studies met inclusion criteria. Of these, 21 were from the field of gastroenterology, 5 from cardiology, 3 from pulmonology, and 3 from interventional radiology. Overall, 22 studies described the development or testing of peer scoring systems and 18 reported inter-observer agreement, which was good or excellent in all but 2 studies. Only 4 studies, all from gastroenterology, tested the association of scoring systems with other quality measures, and no studies tested the impact of peer evaluation on patient outcomes. Best practices included standardized scoring systems, prospective criteria for case selection, and collaborative and non-judgmental review. CONCLUSIONS: Peer evaluation of invasive medical procedures is feasible and generally demonstrates good or excellent inter-observer agreement when performed with structured tools. Our review identifies common elements of successful interventions across specialties. However, there is limited evidence that peer-evaluated performance is linked to other quality measures or that feedback to clinicians improves patient care or outcomes. Additional research is needed to develop and test peer evaluation and feedback interventions.

Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.

BACKGROUND: Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role. OBJECTIVE: To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors. METHODS: A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, (1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method. RESULTS: By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors. CONCLUSIONS: Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future.

Relapse Prevention with Tyrosine Kinase Inhibitors after Allogeneic Transplantation for Philadelphia Chromosome-Positive Acute Lymphoblast Leukemia: A Systematic Review.

Relapse after stem cell transplantation for Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) remains a significant challenge. In this systematic review, we compare survival outcomes of second-generation tyrosine kinase inhibitors (TKIs) nilotinib and dasatinib with first-generation TKI imatinib when these agents are used after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in Ph+ ALL. In addition, we review the literature on TKI use to prevent relapse in patients who proceed to allo-HSCT beyond first complete response (>CR1). We performed database searches (inception to January 2018) using PubMed, Cochrane Library, and Embase. After exclusions, 17 articles were included in this analysis. Imatinib was used post-transplant either prophylactically or preemptively in 12 studies, 7 prospective studies and 5 retrospective studies. Overall survival (OS) for most prospective studies at 1.5 to 3 and 5 years ranged between 62% to 92% and 74.5% to 86.7%. Disease-free survival at 1.5 to 5 years was 60.4% to 92%. Additionally, imatinib failed to show survival benefit in patients who were >CR1 at the time of allo-HSCT. The cumulative OS for most retrospective studies using imatinib at 1 to 2 and 3 to 5 years was 42% to 100% and 33% to 40% respectively. Event-free survival at 1 to 2 and 3 to 5 years was 33.3% to 67% and 20% to 31% respectively. Dasatinib was used as maintenance treatment in 3 retrospective studies (n = 34). The OS for patients with Ph+ ALL using dasatinib as maintenance regimen after allo-HSCT at 1.4 to 3 years was 87% to 100% and disease-free survival at 1.4 to 3 years was 89% to 100%. Ninety-three percent of patients with minimal residual disease (MRD) positive status after allo-HSCT became MRD negative. Three prospective studies used nilotinib. In 2 studies where investigators studied patients with advanced chronic myeloid leukemia and Ph+ ALL, the cumulative OS and event-free survival at 7.5 months to 2 years were 69% to 84% and 56% to 84%, respectively. In the third study (n = 5) in patients with Ph+ ALL, nilotinib use resulted in OS at 5 years of 60%. Our review showed that use of TKIs (all generations) after allo-HSCT for patients in CR1 improved OS when given as a prophylactic or preemptive regimen. Limited data suggest that second-generation TKIs (ie, dasatinib) have a better OS, especially in patients with MRD-positive status. Imatinib did not improve OS in patients who were >CR1 at the time of allo-HSCT; for this population, no data were available with newer generation TKIs. The evaluation of survival benefit with newer generation TKIs and their efficacy in patients in >CR1 needs further study in large randomized clinical trials.

Iron and Ferritin Deposition in the Ovarian Tissues of the Yellow Fever Mosquito (Diptera: Culicidae).

Dengue, yellow fever, and Zika are viruses transmitted by yellow fever mosquito, Aedes aegypti [Linnaeus (Diptera: Culicidae)], to thousands of people each year. Mosquitoes transmit these viruses while consuming a blood meal that is required for oogenesis. Iron, an essential nutrient from the blood meal, is required for egg development. Mosquitoes receive a high iron load in the meal; although iron can be toxic, these animals have developed mechanisms for dealing with this load. Our previous research has shown iron from the blood meal is absorbed in the gut and transported by ferritin, the main iron transport and storage protein, to the ovaries. We now report the distribution of iron and ferritin in ovarian tissues before blood feeding and 24 and 72 h post-blood meal. Ovarian iron is observed in specific locations. Timing post-blood feeding influences the location and distribution of the ferritin heavy-chain homolog, light-chain homolog 1, and light-chain homolog 2 in ovaries. Understanding iron deposition in ovarian tissues is important to the potential use of interference in iron metabolism as a vector control strategy for reducing mosquito fecundity, decreasing mosquito populations, and thereby reducing transmission rates of vector-borne diseases.

Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.

BACKGROUND: To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome. METHODS: A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients. RESULTS: The correlation coefficients between automated and radiologist's manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients' PFS (p < 0.01). CONCLUSION: Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.

Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.

BACKGROUND: In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge. PURPOSE: To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to chemotherapy in clinical trials. MATERIAL AND METHODS: A dataset of 30 patients was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment tumors depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the tumor volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict patients' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in Solid Tumors (RECIST) guideline. RESULTS: The areas under receiver operating characteristic curve (AUC) were 0.773 ± 0.086, 0.680 ± 0.109, and 0.668 ± 0.101, when using each of three features, respectively. AUC value increased to 0.831 ± 0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%). CONCLUSION: This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of ovarian cancer patients to the chemotherapy in clinical trials.

Measurements of adiposity as clinical biomarkers for first-line bevacizumab-based chemotherapy in epithelial ovarian cancer.

OBJECTIVE: There is a lack of reliable indicators to predict who will benefit most from anti-angiogenic therapy, such as bevacizumab. Recognizing obesity is associated with increased levels of VEGF, the main target of bevacizumab, we sought to assess if adiposity, measured in terms of BMI, subcutaneous fat area (SFA), and visceral fat area (VFA) was prognostic. METHODS: Reviewed 46 patients with advanced EOC who received primary treatment with bevacizumab-based chemotherapy (N=21) or chemotherapy alone (N=25) for whom complete records, CT prior to the first cycle of chemo, and serum were available. CT was used to measure SFA and VFA by radiologists blinded to outcomes. ELISA was used to measure serum levels of VEGF and angiopoietin-2 in the bevacizumab group. RESULTS: BMI, SFA, and VFA were dichotomized using the median and categorized as "high" or "low". In the bevacizumab group median PFS was shorter for patients with high BMI (9.8 vs. 24.7months, p=0.03), while in the chemotherapy group median PFS was similar between high and low BMI (17.6 vs. 11.9months, p=0.19). In the bevacizumab group patients with a high BMI had higher median levels of VEGF and angiopoietin-2, 371.9 vs. 191.4pg/ml (p=0.05) and 45.9 vs. 16.6pg/ml (p=0.09) respectively. On multivariate analysis neither BMI, SFA, nor VFA were associated with PFS (p=0.13, p=0.86, p=0.16 respectively) or OS (p=0.14, p=0.93, p=0.28 respectively) in the chemotherapy group. However, in the bevacizumab group BMI was significantly associated with PFS (p=0.02); accounting for confounders adjusted HR for high vs. low BMI was 5.16 (95% CI 1.31-20.24). Additionally in the bevacizumab group SFA was significantly associated with OS (p=0.03); accounting for confounders adjusted HR for high vs. low SFA was 3.58 (95% CI 1.12-11.43). CONCLUSION: Results provide the first evidence in EOC that patients with high levels of adiposity may not derive benefit from bevacizumab and that measurements of adiposity are likely to be a useful biomarker.

Prospective Evaluation of Venous Excess Ultrasound for Estimation of Venous Congestion.

BACKGROUND: Venous excess ultrasound (VExUS) is a novel ultrasound technique previously reported as a noninvasive measure of venous congestion and predictor of cardiorenal acute kidney injury. RESEARCH QUESTION: Are there associations between VExUS grade and cardiac pressures measured by right heart catheterization (RHC) and cardiac biomarkers and clinical outcomes in patients undergoing RHC? STUDY DESIGN AND METHODS: We conducted a prospective cohort study at the Denver Health Medical Center from December 20, 2022, to March 25, 2023. All patients undergoing RHC underwent a blinded VExUS assessment prior to their procedure. Multivariable regressions were conducted to assess relationships between VExUS grade and cardiac pressures, biomarkers, and changes in weight among patients with heart failure, a proxy for diuretic success. Receiver operating characteristic curve and area under the curve (AUC) were derived for VExUS, inferior vena cava (IVC) diameter, and IVC collapsibility index (ICI) to predict right atrial pressure (RAP) > 10 and < 7 mm Hg. RESULTS: Among 81 patients, 45 of whom were inpatients, after adjusting for age, sex, and Charlson Comorbidity Index, there were significant relationships between VexUS grade of 2 (ß = 4.8; 95% CI, 2.6-7.1; P < .01) and 3 (ß = 11; 95% CI, 8.9-14; P < .01) and RAP, VExUS grade of 2 (ß = 6.8; 95% CI, 0.16-13; P = .045) and 3 (ß = 15; 95% CI, 7.3-22; P < .01) and mean pulmonary artery pressure, and VExUS grade of 2 (ß = 7.0; 95% CI, 3.9-10; P < .01) and 3 (ß = 13; 95% CI, 9.5-17; P < .01) and pulmonary capillary wedge pressure. AUC values for VExUS, IVC diameter, and ICI as predictors of RAP > 10 mm Hg were 0.9 (95% CI, 0.83-0.97), 0.77 (95% CI, 0.68-0.88), and 0.65 (95% CI, 0.52-0.78), respectively. AUC values for VExUS, IVC diameter, and ICI as predictors of RAP < 7 mm Hg were 0.79 (95% CI, 0.70-0.87), 0.74 (95% CI, 0.64-0.84), and 0.62 (95% CI, 0.49-0.76), respectively. In a subset of 23 patients with heart failure undergoing diuresis, there was a significant association between VExUS grade 3 and change in weight between time of RHC and discharge (P = .025). INTERPRETATION: Although more research is required, VExUS has the potential to increase diagnostic and therapeutic capabilities of physicians at the bedside and increase our understanding of the underappreciated problem of venous congestion.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS: The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

Venous excess ultrasonography (VExUS) captures dynamic changes in volume status surrounding hemodialysis: A multicenter prospective observational study.

Background: The evaluation of volume status is essential to clinical decision-making, yet multiple studies have shown that physical exam does not reliably estimate a patient's intravascular volume. Venous excess ultrasound score (VExUS) is an emerging volume assessment tool that utilizes inferior vena cava (IVC) diameter and pulse-wave Doppler waveforms of the portal, hepatic and renal veins to evaluate venous congestion. A point-of-care ultrasound exam initially developed by Beaubein-Souligny et al., VExUS represents a reproducible, non-invasive and accurate means of assessing intravascular congestion. VExUS has recently been validated against RHC-the gold-standard of hemodynamic evaluation for volume assessment. While VExUS scores were shown to correlate with elevated cardiac filling pressures (i.e., right atrial pressure (RAP) and pulmonary capillary wedge pressure (PCWP)) at a static point in time, the ability of VExUS to capture dynamic changes in volume status has yet to be elucidated. We hypothesized that paired VExUS examinations performed before and after hemodialysis (HD) would reflect changes in venous congestion in a diverse patient population. Methods: Inpatients with end-stage renal disease undergoing intermittent HD were evaluated with transabdominal VExUS and lung ultrasonography before and following HD. Paired t-tests were conducted to assess differences between pre-HD and post-HD VExUS scores, B-line scores and dyspnea scores. Results: Fifty-six patients were screened for inclusion in this study. Ten were excluded due to insufficient image quality or incomplete exams, and forty-six patients (ninety-two paired ultrasound exams) were included in the final analysis. Paired t-test analysis of pre-HD and post-HD VExUS scores revealed a mean VExUS grade change of 0.82 (p<0.001) on a VExUS scale ranging from 0 to 4. The mean difference in B-line score following HD was 0.8 (p=0.001). There was no statistically significant difference in subjective dyspnea score (p=0.41). Conclusions: Large-volume fluid removal with HD was represented by changes in VExUS score, highlighting the utility of the VExUS exam to capture dynamic shifts in intravascular volume status. Future studies should evaluate change in VExUS grade with intravenous fluid or diuretic administration, with the ultimate goal of evaluating the capacity of a standardized bedside ultrasound protocol to guide inpatient volume optimization.

MRI manifestations of pancreatic disease, especially pancreatitis, in the pediatric population.

OBJECTIVE: Although pancreatitis is an uncommon entity in children, the pediatric population can develop serious and long-lasting complications, including pseudocyst, necrosis, hemorrhage, vascular thrombosis, vascular pseudoaneurysm, abscess, and pancreaticopleural fistula. CT has historically been the mainstay for noninvasive imaging of the pancreas. This modality is limited in the pediatric population because of poorly developed retroperitoneal fat planes, difficulty in evaluating the ductal anatomy, and the use of ionizing radiation. MRI with MRCP provides superior soft-tissue resolution and improved visualization of ductal anatomy and can delineate complications of pancreatitis, while avoiding exposure to potentially harmful radiation. CONCLUSION: For these reasons, we advocate abdominal MRI with MRCP as the preferred modality for pancreatic evaluation in the pediatric population. The purpose of this article is to briefly discuss the normal anatomy and embryologic development of the pancreas, review standard sequences for routine abdominal MRI and MRCP in pediatric patients, discuss the normal appearance of the pancreas and biliary tree on MRI sequences, and use examples to illustrate the MRI appearance of common and uncommon manifestations of pancreatic disease in pediatric patients.

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. RESULTS: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction.

Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.

Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.

Recent advances and clinical applications of deep learning in medical image analysis.

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss major technical challenges and suggest possible solutions in the future research efforts.

The role of chest computed tomography in the management of COVID-19: A review of results and recommendations.

IMPACT STATEMENT: The impact of the COVID-19 pandemic has been worldwide, and clinicians and researchers around the world have been working to develop effective and efficient methods for early detection as well as monitoring of the disease progression. This minireview compiles the various agency and expert recommendations, along with results from studies published in numerous countries, in an effort to facilitate the research in imaging technology development to benefit the detection and monitoring of COVID-19. To the best of our knowledge, this is the first review paper on the topic, and it provides a brief, yet comprehensive analysis.

Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients.

BACKGROUND AND OBJECTIVE: In diagnosis of cervical cancer patients, lymph node (LN) metastasis is a highly important indicator for the following treatment management. Although CT/PET (i.e., computed tomography/positron emission tomography) examination is the most effective approach for this detection, it is limited by the high cost and low accessibility, especially for the rural areas in the U.S.A. or other developing countries. To address this challenge, this investigation aims to develop and test a novel radiomics-based CT image marker to detect lymph node metastasis for cervical cancer patients. METHODS: A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, the prediction models (i.e., logistic regression or support vector machine) were trained and optimized to generate an image marker to detect LN metastasis. In this study, a retrospective dataset containing 127 cervical cancer patients were established to build and test the model. The model was trained using a leave-one-case-out (LOCO) cross-validation strategy and image marker performance was evaluated using the area under receiver operation characteristic (ROC) curve (AUC). RESULTS: The results indicate that the SVM based imaging marker achieved an AUC value of 0.841 ± 0.035. When setting an operating threshold of 0.5 on model-generated prediction scores, the imaging marker yielded a positive and negative predictive value (PPV and NPV) of 0.762 and 0.765 respectively, while the total accuracy is 76.4%. CONCLUSIONS: This study initially verified the feasibility of utilizing CT image and radiomics technology to develop a low-cost image marker to detect LN metastasis for assisting stratification of cervical cancer patients.

Hepatic Actinomycosis in a Patient With Retained Common Bile Duct Stent.

Primary hepatic actinomycosis is rare, with less than 100 cases reported in English literature. Most of these cases are cryptogenic. We describe a 35-year-old woman who presented with a retained common bile duct stent for 6 years and found to have a hepatic mass with altered perfusion and enhancement, and minimal degree of washout on enhanced cross-sectional imaging. Fine-needle aspiration revealed presence of filamentous bacteria morphologically consistent with Actinomyces species. This report is a demonstration of a rare instance in which a retained biliary stent led to primary hepatic actinomycosis.

Uterine Didelphys with Bilateral Cervical Agenesis in a 15-Year-Old Girl.

BACKGROUND: Isolated uterine didelphys requires no treatment in contrast to cervical agenesis, which requires a hysterectomy. Because of this, correct diagnosis of Müllerian anomalies is paramount for making recommendations for patient care. CASE: A 15-year-old girl presented to clinic with pelvic pain and primary amenorrhea. Uterine didelphys with bilateral cervical agenesis was diagnosed using imaging. Hysterectomy was recommended and diagnosis was confirmed at surgery and according to anatomic pathology. SUMMARY AND CONCLUSION: Our patient with uterine didelphys with bilateral cervical agenesis presented a diagnostic challenge, because, to our knowledge, it has never been reported before in the literature. Her pattern of anomalies had significant implications for future fertility. Radiology exam was vital to confirming this diagnosis in a young, virginal female patient.