Citations

AbstractHospitals, payors, and patients increasingly expect us to report our outcomes in more detail and to justify our treatment decisions and costs. Although there are many stakeholders in surgical outcomes, physicians must take the lead role in defining how outcomes are assessed. Skull base lesions interact with surrounding anatomy to produce a complex spectrum of presentations and surgical challenges, requiring a wide variety of surgical approaches. Moreover, many skull base lesions are relatively rare. These factors and others often preclude the use of prospective randomized clinical trials, thus necessitating alternate methods of scientific inquiry. In this paper, we propose a roadmap for implementing a skull base registry, along with expected benefits and challenges.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Abstract Background Each training program has its own internal policies and restrictions, which must be considered while developing trainee schedules. Designing these schedules is complex and time consuming, and the final schedules often contain undesirable aspects for trainees. Objective We developed a decision-support system (DSS) to optimally schedule daily assignments and monthly rotations for trainees. The proposed DSS aims to 1) reduce the schedule development time, 2) maximize trainee preferences for desired rotations and vacation times, and 3) ensure adaptability of the DSS across multiple graduate medical programs through a flexible design and intuitive graphical user interface. Methods Using mixed-integer linear programming, we developed a scheduling model that 1) maximized trainees’ preferences on specific rotations and vacation times and 2) ensured fairness by assigning equal numbers of vacation days and a balanced schedule of difficult versus easy rotations among trainees. The model was successfully implemented in the Mayo Clinic Division of Pulmonary and Critical Care for the academic year 2018–2019. Results Using the DSS, it took only a few minutes to produce a schedule versus several days of preparation time required by the manual process. Compared with the manually developed schedule, the DSS schedule satisfied 11% more rotation preferences and improved fairness by 19%. All trainees met duty hours in the DSS schedule compared with 83% in the manually developed schedule. Conclusion The proposed DSS can dramatically reduce the schedule preparation time, accommodate more of trainees’ preferences, and improve fairness in assigning rotations.

Abstract Background: Although psoriasis is considered a systemic disease, no clear association has been established between psoriasis and lung diseases. This study aims to detect and describe subclinical pulmonary involvement in psoriasis patients with various degrees of cutaneous manifestations.Methods: Adult psoriasis patients with no known active pulmonary disease or respiratory symptoms were screened for subclinical pulmonary manifestations and possible parenchymal changes using high-resolution computed tomography (HRCT) scan of the chest. Patients were classified according to the severity of skin manifestations. The clinical characteristics and radiographic findings of these patients were evaluated.Results: Fifty-nine patients with psoriasis were included, among which 47 (79.7%) had abnormal HRCT scan features. Micronodules were the most common detected lung lesions (66.1%), followed by nonspecific interstitial changes (32.2%), including pleuro-parenchymal band/atelectasis, scarring, and focal ground-glass opacities. Other HRCT findings included emphysematous changes and calcified granulomas. Abnormal HRCT findings correlated with older age and duration of psoriasis but not with the severity of skin manifestations.Conclusions: Micronodules and minor focal nonspecific interstitial changes were the most detected lung alterations in patients with psoriasis. These findings of the pilot study highlight a possible pulmonary involvement in patients with psoriasis. Larger multicenter studies are needed to clarify these findings further.

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Abstract Objectives Peritoneal metastasis (PM) from appendiceal cancer or colorectal cancer (CRC) has significant morbidity and limited survival. Pressurized intraperitoneal aerosolized chemotherapy (PIPAC) is a minimally invasive approach to treat PM. We aim to conduct a dose-escalation trial of mitomycin C (MMC)-PIPAC combined with systemic chemotherapy (FOLFIRI) in patients with PM from appendiceal cancer or CRC. Methods This is a multicenter Phase I study of MMC-PIPAC (NCT04329494). Inclusion criteria include treatment with at least 4 months of first- or second-line systemic chemotherapy with ineligibility for cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). Exclusion criteria are: progression on chemotherapy; extraperitoneal metastases; systemic chemotherapy intolerance; bowel obstruction; or poor performance status (ECOG>2). Escalating MMC-PIPAC doses (7–25 mg/m 2 ) will be administered in combination with standard dose systemic FOLFIRI. Safety evaluation will be performed on 15 patients (dose escalation) and six expansion patients: 21 evaluable patients total. Results The primary endpoints are recommended MMC dose and safety of MMC-PIPAC with FOLFIRI. Secondary endpoints are assessment of response (by peritoneal regression grade score; Response Evaluation Criteria in Solid Tumors [RECIST 1.1], and peritoneal carcinomatosis index), progression free survival, overall survival, technical failure rate, surgical complications, conversion to curative-intent CRS-HIPEC, patient-reported outcomes, and functional status. Longitudinal blood and tissue specimens will be collected for translational correlatives including pharmacokinetics, circulating biomarkers, immune profiling, and single-cell transcriptomics. Conclusions This Phase I trial will establish the recommended dose of MMC-PIPAC in combination with FOLFIRI. Additionally, we expect to detect an early efficacy signal for further development of this therapeutic combination.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Abstract Brain metastases are a challenging manifestation of renal cell carcinoma. We have a limited understanding of brain metastasis tumor and immune biology, drivers of resistance to systemic treatment, and their overall poor prognosis. Current data support a multimodal treatment strategy with radiation treatment and/or surgery. Nonetheless, the optimal approach for the management of brain metastases from renal cell carcinoma remains unclear. To improve patient care, the authors sought to standardize practical management strategies. They performed an unstructured literature review and elaborated on the current management strategies through an international group of experts from different disciplines assembled via the network of the International Kidney Cancer Coalition. Experts from different disciplines were administered a survey to answer questions related to current challenges and unmet patient needs. On the basis of the integrated approach of literature review and survey study results, the authors built algorithms for the management of single and multiple brain metastases in patients with renal cell carcinoma. The literature review, consensus statements, and algorithms presented in this report can serve as a framework guiding treatment decisions for patients. CA Cancer J Clin. 2022;72:454‐489.