Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly. METHODS: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results. RESULTS: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation. CONCLUSIONS: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition.

First-line nivolumab plus ipilimumab with two cycles of chemotherapy versus chemotherapy alone (four cycles) in metastatic non-small cell lung cancer: CheckMate 9LA 2-year patient-reported outcomes.

BACKGROUND: In CheckMate 9LA (NCT03215706), first-line nivolumab plus ipilimumab with chemotherapy (2 cycles) significantly improved overall survival versus chemotherapy (4 cycles) in patients with metastatic non-small cell lung cancer and no known sensitising epidermal growth factor receptor/anaplastic lymphoma kinase alterations. We present exploratory patient-reported outcomes (PROs; minimum follow-up, 2 years). METHODS: In patients (N = 719) randomised 1:1 to nivolumab plus ipilimumab with chemotherapy or chemotherapy alone, disease-related symptom burden and health-related quality of life were assessed using the Lung Cancer Symptom Scale (LCSS) and 3-level EQ-5D (EQ-5D-3L). Treatment-phase changes in LCSS average symptom burden index (ASBI), LCSS three-item global index (3-IGI) and EQ-5D-3L visual analogue scale (VAS) and utility index (UI) over time were analysed descriptively and using mixed-effect model repeated measures. Time-to-deterioration/improvement analyses were conducted. RESULTS: Treatment-phase PRO questionnaire completion rates were >80%. Mean treatment-phase changes showed no deterioration from baseline in both arms for LCSS ASBI/3-IGI and EQ-5D-3L VAS/UI; however, minimally important differences were not met. Mixed-effect model repeated measures analyses showed overall reduction in symptom burden from baseline for both arms; changes from baseline for LCSS 3-IGI and EQ-5D-3L VAS/UI were numerically improved with nivolumab plus ipilimumab with chemotherapy versus chemotherapy, but minimally important differences were not met. Nivolumab plus ipilimumab with chemotherapy delayed time-to-definitive-deterioration versus chemotherapy (LCSS ASBI: hazard ratio, 0.62 [95% confidence interval, 0.45-0.87]); results were similar across PRO measures. CONCLUSIONS: At 2-year minimum follow-up, first-line nivolumab plus ipilimumab with chemotherapy reduced the risk of definitive deterioration in disease-related symptom burden and health-related quality of life versus chemotherapy and maintained QoL in patients with metastatic non-small cell lung cancer. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier, NCT03215706.

First-line nivolumab plus ipilimumab versus chemotherapy for the treatment of unresectable malignant pleural mesothelioma: patient-reported outcomes in CheckMate 743.

OBJECTIVE: In CheckMate 743 (NCT02899299), nivolumab + ipilimumab significantly prolonged overall survival in patients with unresectable malignant pleural mesothelioma (MPM). We present patient-reported outcomes (PROs). MATERIALS AND METHODS: Patients (N = 605) were randomized to nivolumab + ipilimumab or chemotherapy. Changes in disease-related symptom burden and health-related quality of life (HRQoL) were evaluated descriptively using the Lung Cancer Symptom Scale (LCSS)-Mesothelioma (Meso) average symptom burden index (ASBI), LCSS-Meso 3-item global index (3-IGI), 3-level EuroQol 5-dimensional (EQ-5D-3L) visual analog score (VAS), and EQ-5D-3L utility index. PROs were assessed at baseline and every 2 (nivolumab + ipilimumab) or 3 weeks (chemotherapy) through 12 weeks, every 6 weeks through 12 months, every 12 weeks thereafter, and at specified follow-ups. Mixed-effect model repeated measures (MMRM) and time to deterioration analyses were conducted. RESULTS: Completion rates were generally >80%. LCSS-Meso ASBI mean changes from baseline trended to improve over time with nivolumab + ipilimumab and deteriorate with chemotherapy, but did not meet clinically important difference thresholds [±10 score change]. EQ-5D-3L VAS mean scores improved over time with nivolumab + ipilimumab; by week 60, patients had scores consistent with United Kingdom normal population values. MMRM analyses favored nivolumab + ipilimumab for all individual symptoms except cough. Nivolumab + ipilimumab delayed time to definitive deterioration in HRQoL (hazard ratio 0.52 [95% confidence interval 0.36-0.74]) and showed a trend in symptom delay versus chemotherapy. CONCLUSIONS: Nivolumab + ipilimumab decreased the risk of deterioration in disease-related symptoms and HRQoL versus chemotherapy and maintained QoL in patients with unresectable MPM.

Three-year follow-up and patient-reported outcomes from CheckMate 078: Nivolumab versus docetaxel in a predominantly Chinese patient population with previously treated advanced non-small cell lung cancer.

OBJECTIVES: In the phase 3 CheckMate 078 study, nivolumab prolonged overall survival (OS) and showed a favorable safety profile versus docetaxel in a predominantly Chinese patient population with previously treated advanced non-small cell lung cancer (aNSCLC). However, long-term efficacy, safety, and health-related quality of life findings with second-line nivolumab are very limited in Asian patients with previously treated aNSCLC. Here, we report updated clinical data and patient-reported outcomes (PROs) from the phase 3 CheckMate 078 trial with a 3-year minimum follow-up. MATERIALS AND METHODS: Patients with aNSCLC and disease progression after platinum-doublet chemotherapy were randomized 2:1 to nivolumab (3 mg/kg every 2 weeks) or docetaxel (75 mg/m2 every 3 weeks) until progression or unacceptable toxicity. The primary endpoint was OS; secondary endpoints included objective response rate, progression-free survival, safety, and disease-related symptom deterioration assessed using the Lung Cancer Symptom Scale (LCSS) by Week 12. Additional PRO assessments were exploratory endpoints. RESULTS: At ≥ 37.3 months follow-up, 3-year OS rates were 19% with nivolumab and 12% with docetaxel; 30% and 0% of responders remained in response for ≥ 3 years, respectively. Incidence of treatment-related adverse events occurring after 2 years was lower than during the first 2 years. No new treatment-related deaths were reported. By Week 12 of treatment, rates of disease-related symptom deterioration were 32% with nivolumab and 47% with docetaxel. Completion rates for PRO questionnaires were ≥ 80% in both arms. Clinically meaningful and sustained improvements in LCSS Average Symptom Burden Index scores and delayed time to first symptom deterioration were observed with nivolumab against docetaxel. CONCLUSIONS: At 3 years, nivolumab continued to demonstrate survival benefit versus docetaxel, exhibiting improvements in disease-related symptoms and overall health status in a predominantly Chinese patient population with previously treated aNSCLC. No new safety signals were observed. These findings are similar to the global population.

First-Line Nivolumab Plus Ipilimumab Versus Chemotherapy in Advanced NSCLC With 1% or Greater Tumor PD-L1 Expression: Patient-Reported Outcomes From CheckMate 227 Part 1.

INTRODUCTION: In CheckMate 227 (NCT02477826), patients with treatment-naive stage IV or recurrent NSCLC and 1% or greater tumor programmed death ligand 1 expression had significantly improved overall survival with nivolumab plus ipilimumab versus chemotherapy. We present the patient-reported outcomes (PROs). METHODS: Patients (N = 1189) were randomized to nivolumab plus ipilimumab, nivolumab, or chemotherapy. PROs were exploratory. Changes in Lung Cancer Symptom Scale (LCSS) average symptom burden index, LCSS 3-item global index, EQ-5D visual analog scale (VAS), and EQ-5D utility index were analyzed descriptively. Mixed-effect model repeated measures and time-to-first deterioration and improvement analyses were conducted. RESULTS: PRO completion rates were generally greater than 80%. On-treatment improvements from baseline in LCSS measures of symptom burden and global health status with nivolumab plus ipilimumab generally met or exceeded the minimal important difference (smallest clinically meaningful change) from weeks 24 and 30, respectively; improvements with chemotherapy generally remained below the minimal important difference. Mean on-treatment EQ-5D VAS scores for both treatments approached the U.K. population norm at week 24, remaining so throughout the treatment period. Mixed-effect model repeated measures analyses revealed numerically greater improvements from baseline with nivolumab plus ipilimumab versus chemotherapy across LCSS average symptom burden index and 3-item global index, and EQ-5D VAS and utility index. Nivolumab plus ipilimumab had delayed time-to-first deterioration (hazard ratio [95% confidence interval] 0.74 [0.56 to 0.98]) and a trend for more rapid time-to-first improvement (1.24 [0.98 to 1.59]) versus chemotherapy. CONCLUSIONS: Nivolumab plus ipilimumab revealed delayed deterioration and numerical improvement in symptoms and health-related quality of life versus chemotherapy in patients with advanced NSCLC and 1% or greater programmed death ligand 1 expression.

Diagnosis of Chronic Pancreatitis Incorporating Endosonographic Features, Demographics, and Behavioral Risk.

OBJECTIVES: Diagnosing chronic pancreatitis remains challenging. Endoscopic ultrasound (EUS) is utilized to evaluate pancreatic disease. Abnormal pancreas function test is considered the "nonhistologic" criterion standard for chronic pancreatitis. We derived a prediction model for abnormal endoscopic pancreatic function test (ePFT) by enriching EUS findings with patient demographic and pancreatitis behavioral risk characteristics. METHODS: Demographics, behavioral risk characteristics, EUS findings, and peak bicarbonate results were collected from patients evaluated for pancreatic disease. Abnormal ePFT was defined as peak bicarbonate of less than 75 mEq/L. We fit a logistic regression model and converted it to a risk score system. The risk score was validated using 1000 bootstrap simulations. RESULTS: A total of 176 patients were included; 61% were female with median age of 48 years (interquartile range, 38-57 years). Abnormal ePFT rate was 39.2% (69/176). Four variables formulated the risk score: alcohol or smoking status, number of parenchymal abnormalities, number of ductal abnormalities, and calcifications. Abnormal ePFT occurred in 10.7% with scores 4 or less versus 92.0% scoring 20 or greater. The model C-statistic was 0.78 (95% confidence interval, 0.71-0.85). CONCLUSIONS: Number of EUS pancreatic duct and parenchymal abnormalities, presence of calcification, and smoking/alcohol status were predictive of abnormal ePFT. This simple model has good discrimination for ePFT results.

Randomized trial outcomes of a TTM-tailored condom use and smoking intervention in urban adolescent females.

Smoking and sexual risk behaviors in urban adolescent females are prevalent and problematic. Family planning clinics reach those who are at most risk. This randomized effectiveness trial evaluated a transtheoretical model (TTM)-tailored intervention to increase condom use and decrease smoking. At baseline, a total of 828 14- to 17-year-old females were recruited and randomized within four urban family planning clinics. Participants received TTM or standard care (SC) computerized feedback and stage-targeted or SC counseling at baseline, 3, 6 and 9 months. Blinded follow-up telephone surveys were conducted at 12 and 18 months. Analyses revealed significantly more consistent condom use in the TTM compared with the SC group at 6 and 12, but not at 18 months. In baseline consistent condom users (40%), significantly less relapse was found in the TTM compared with the SC group at 6 and 12, but not at 18 months. No significant effects for smoking prevention or cessation were found, although cessation rates matched those found previously. This TTM-tailored intervention demonstrated effectiveness for increasing consistent condom use at 6 and 12 months, but not at 18 months, in urban adolescent females. This intervention, if replicated, could be disseminated to promote consistent condom use and additional health behaviors in youth at risk.

Baseline stage, severity, and effort effects differentiate stable smokers from maintainers and relapsers.

This cross-sectional study (N = 4,144) compared three longitudinal dynatypes (Maintainers, Relapsers, and Stable Smokers) of smokers on baseline demographics, stage, addiction severity, and transtheoretical model effort effect variables. There were significant small-to-medium-sized differences between the Stable Smokers and the other two groups on stage, severity, and effort effect variables in both treatment and control groups. There were few significant, very small differences on baseline effort variables between Maintainers and Relapsers in the control, but not the treatment group. The ability to identify Stable Smokers at baseline could permit enhanced tailored treatments that could improve population cessation rates.

Development and validation of a disease-specific risk adjustment system using automated clinical data.

OBJECTIVE: To develop and validate a disease-specific automated inpatient mortality risk adjustment system primarily using computerized numerical laboratory data and supplementing them with administrative data. To assess the values of additional manually abstracted data. METHODS: Using 1,271,663 discharges in 2000-2001, we derived 39 disease-specific automated clinical models with demographics, laboratory findings on admission, ICD-9 principal diagnosis subgroups, and secondary diagnosis-based chronic conditions. We then added manually abstracted clinical data to the automated clinical models (manual clinical models). We compared model discrimination, calibration, and relative contribution of each group of variables. We validated these 39 models using 1,178,561 discharges in 2004-2005. RESULTS: The overall mortality was 4.6 percent (n = 58,300) and 4.0 percent (n = 47,279) for derivation and validation cohorts, respectively. Common mortality predictors included age, albumin, blood urea nitrogen or creatinine, arterial pH, white blood counts, glucose, sodium, hemoglobin, and metastatic cancer. The average c-statistic for the automated clinical models was 0.83. Adding manually abstracted variables increased the average c-statistic to 0.85 with better calibration. Laboratory results displayed the highest relative contribution in predicting mortality. CONCLUSIONS: A small number of numerical laboratory results and administrative data provided excellent risk adjustment for inpatient mortality for a wide range of clinical conditions.

Candidemia on presentation to the hospital: development and validation of a risk score.

INTRODUCTION: Candidemia results in substantial morbidity and mortality, especially if initial antifungal therapy is delayed or is inappropriate; however, candidemia is difficult to diagnose because of its nonspecific presentation. METHODS: To develop a risk score for identifying hospitalized patients with candidemia, we performed a retrospective analysis of a large database of 176 acute-care hospitals in the United States. We studied 64,019 patients with bloodstream infection (BSI) on presentation from 2000 through 2005 (derivation cohort) and 24,685 from 2006 to 2007 (validation cohort). We used recursive partitioning (RPART) to identify the best discriminators for Candida as the cause of BSI. We compared three sets of models (equal-weight, unequal-weight, vs full model with additional variables from logistic regression model) for sensitivity analysis. RESULTS: The RPART identified 6 variables as the best discriminators: age < 65 years, temperature 0.10, indicating predicted and observed candidemia rates did not differ significant across the 7 risk stratus). The full model with 16 risk factors had slightly higher AUROCs (0.74 versus 0.73 for derivation versus validation); however, 7 variables were no longer significant in the recalibrated model for the validation cohort, indicating that the additional items did not materially enhance the model. CONCLUSIONS: A simple equal-weight risk score differentiated patients' risk for candidemia in a graded fashion upon hospital presentation.

Cancer prevention in primary care: predictors of patient counseling across four risk behaviors over 24 months.

OBJECTIVE: Rates of preventive counseling remain below national guidelines. We explored physician and patient predictors of preventive counseling across multiple cancer risk behaviors in at-risk primary care patients. METHODS: We surveyed 3557 patients, with at least one of four cancer risk behaviors: smoking, diet, sun exposure, and/or mammography screening, at baseline and 24 months. Patients reported receipt of 4A's (Ask, Advise, Assist, Arrange follow-up); responses were weighted and combined to reflect more thorough counseling (Ask=1, Advise=2, Assist=3, Arrange=4, score range 0-10) for each target behavior. A series of linear-regression models, controlling for office clustering, examined patient, physician and other situational predictors at 24 months. RESULTS: Risk behavior topics were brought up more often for mammography (90%) and smoking (79%) than diet (56%) and sun protection (30%). Assisting and Arranging follow-up were reported at low frequencies across all behaviors. More thorough counseling for all behaviors was associated with multiple visits and higher satisfaction with care. Prior counseling predicted further counseling on all behaviors except smoking, which was already at high levels. Other predictors varied by risk behavior. CONCLUSIONS: More thorough risk behavior counseling can be delivered opportunistically across multiple visits; doing so is associated with more satisfaction with care.

Demographic variables, smoking variables, and outcome across five studies.

OBJECTIVE: Intervention effectiveness can potentially be affected by membership in different demographic subgroups (race, ethnicity, gender, age, and education level) or smoking behavior variables (time to first cigarette, longest previous quit attempt, number of attempts in the past year, number of cigarettes, and stage of change). Previous research on these 2 sets of variables has produced mixed results. DESIGN: This secondary data analysis combined data from 5 effectiveness trials (a random-digit-dial sample [N=1,358], members of an HMO [N=207], parents of students recruited for a school-based study [N=347], patients from an insurance provider list [N=535], and employees [N=175]) in which smokers were all proactively recruited from a defined population and all received the same expert system intervention. The intervention produced a consistent 22% to 26% point prevalence cessation rate across the 5 studies. MAIN OUTCOME MEASURES: The main outcome measures were 24-hr point prevalence, 7-day point prevalence, 30-day prolonged abstinence, and 6-month prolonged abstinence. RESULTS: There were no significant differences in outcome across gender, race, and ethnicity subgroups. There were significant differences and small effect sizes for age and education subgroups. There were significant differences and large effect sizes for all 5 smoking behavior variables. DISCUSSION: Demographic variables are static variables, whereas the smoking variables are more dynamic, that is, open to change. Given the dynamic nature of the smoking variables and the large effect sizes, interventions tailored on the smoking variables should be more successful.

Evaluating nicotine replacement therapy and stage-based therapies in a population-based effectiveness trial.

Pharmacological interventions for smoking cessation are typically evaluated using volunteer samples (efficacy trials) but should also be evaluated in population-based trials (effectiveness trials). Nicotine replacement therapy (NRT) alone and in combination with behavioral interventions was evaluated on a population of smokers from a New England Veterans Affairs Medical Center. Telephone interviews were completed with 3,239 smokers, and 2,054 agreed to participate (64%). Participants were randomly assigned to one of four conditions: stage-matched manuals (MAN); NRT plus manuals (NRT + MAN); expert system plus NRT and manuals (EXP + NRT + MAN); and automated counseling plus NRT, manuals, and expert system (TEL + EXP + NRT + MAN). Assessments were completed at baseline, 10, 20, and 30 months. The point prevalence cessation rates at final follow-up (30 months) were MAN, 20.3%; NRT + MAN, 19.3%; EXP + NRT + MAN, 17.6%; and TEL + EXP + NRT + MAN, 19.9%. Stage-matched manuals provided cessation rates comparable with previous studies. The addition of NRT, expert system interventions, and automated telephone counseling failed to produce a further increase in intervention effectiveness.

Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms.

BACKGROUND: Treating multiple health behavior risks on a population basis is one of the most promising approaches to enhancing health and reducing health care costs. Previous research demonstrated the efficacy of expert system interventions for three behaviors in a population of parents. The interventions provide individualized feedback that guides participants through the stages of change for each of their risk behaviors. This study extended that research to a more representative population of patients from primary care practice and to targeting of four rather than three behaviors. METHODS: Stage-based expert systems were applied to reduce smoking, improve diet, decrease sun exposure, and prevent relapse from regular mammography. A randomized clinical controlled trial recruited 69.2% of primary care patients (N = 5407) at home via telephone. Three intervention contacts were delivered for each risk factor at 0, 6, and 12 months. The primary outcome measures were the percentages of at-risk patients at baseline who progressed to the action or maintenance stages at 24-month follow-up for each of the risk behaviors. RESULTS: Significant treatment effects were found for each of the four behaviors, with 25.4% of intervention patients in action or maintenance for smoking, 28.8% for diet, and 23.4% for sun exposure. The treatment group had less relapse from regular mammography than the control group (6% vs. 10%). CONCLUSION: Proactive, home-based, and stage-matched expert systems can produce relatively high population impacts on multiple behavior risks for cancer and other chronic diseases.

Multiple risk expert systems interventions: impact of simultaneous stage-matched expert system interventions for smoking, high-fat diet, and sun exposure in a population of parents.

Three stage-based expert system interventions for smoking, high-fat diet, and unsafe sun exposure were evaluated in a sample of 2,460 parents of teenagers. Eighty-four percent of the eligible parents were enrolled in a 2-arm randomized control trial, with the treatment group receiving individualized feedback reports for each of their relevant behaviors at 0, 6, and 12 months as well as a multiple behavior manual. At 24 months, the expert system outperformed the comparison condition across all 3 risk behaviors, resulting in 22% of the participants in action or maintenance for smoking (vs. 16% for the comparison condition), 34% for diet (vs. 26%), and 30% for sun exposure (vs. 22%). Proactive, home-based, and stage-matched expert systems can produce significant multiple behavior changes in at-risk populations where the majority of participants are not prepared to change.