Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study.

Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.

Importance: Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images. Objective: To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software. Design, Setting, and Participants: This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance. Interventions: Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition. Main Outcomes and Measures: Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers. Results: A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters. Conclusions and Relevance: This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

Joint modeling of time to recurrence and cancer stage at recurrence in oncology trials.

This research was motivated by a clinical trial with bladder cancer patients who went through a surgery and were followed up for cancer recurrence. One of the main objectives of the trial was to evaluate the time to cancer recurrence in patients in control and experimental groups. At the time of recurrence, the disease stage was also evaluated. Because the stage of cancer at recurrence significantly impacts future treatment and patient prognosis of survival, analyzing the time to cancer recurrence and the stage at recurrence jointly provides more clinically relevant information than analyzing the time to recurrence alone. In this paper, we propose a stochastic model for the joint distribution of time to recurrence and cancer stage that (1) accounts for the recurrence caused by cancer cells surviving a treatment or a surgery and for the recurrence caused by spontaneous carcinogenesis, and (2) incorporates parameters that have biological meaning. To estimate the parameters, we use the maximum-likelihood method combined with the EM algorithm. To demonstrate the performance of our modeling, we evaluate the data from a clinical trial in patients with bladder cancer. We also use simulations to assess the sensitivity of the method.

Fluorescence and white light cystoscopy for detection of carcinoma in situ of the urinary bladder.

OBJECTIVES: To understand the additional benefits of HAL compared with conventional cystoscopy at the patient level and to explore relationships of urine cytology and CIS. METHODS: We reanalyzed pooled data from 3 phase III studies comparing hexaminolevulinate (HAL, Hexvix) fluorescence cystoscopy with white light (WL) cystoscopy for detecting CIS. RESULTS: Of 551 patients, 174 had at least one CIS lesion detected by HAL, WL, or random biopsy. The CIS detection rate of HAL was 0.87 vs. 0.75 for WL (P = 0.006). By multivariate Poisson regression, female patients had fewer CIS lesions (P < 0.0001) while older patients (≥ 65) had a higher number of CIS lesions detected by HAL (P = 0.04). HAL was less likely to detect CIS in patients previously treated with chemotherapy or BCG (P = 0.01 and 0.03, respectively), after adjusting for age. CIS was unifocal in 44% and multifocal in 56%. Multifocal CIS was associated with positive cytology more frequently than unifocal (65% vs. 45%; P = 0.016) whereas a negative cytology was more frequently associated with unifocal CIS. Patients with positive urine cytology had twice as many CIS lesions detected by HAL as patients with negative urine cytology (P = 0.02). CONCLUSIONS: HAL cystoscopy had a higher CIS detection rate than WL cystoscopy. The average number of CIS lesions detected was associated with baseline clinical characteristics. Cytology was positive more frequently in multifocal CIS suggesting that HAL may be particularly useful in this setting to optimize detection of the extent of CIS.

Fluorescence-guided transurethral resection of bladder cancer using hexaminolevulinate: analysis of health economic impact in Sweden.

OBJECTIVE: A decision analytic model was established to estimate the budget impact on the Swedish health service of using hexaminolevulinate (HAL) in conjunction with white light cystoscopy (WLC) in the management of bladder cancer for 1 year following initial diagnosis. MATERIAL AND METHODS: Flowcharts were developed to represent the diagnostic and treatment pathways for each of four risk groups for non-muscle-invasive bladder cancer (NMIBC), as defined by risk of tumour recurrence and progression. Flowcharts were based on European Association of Urology Guidelines and adjusted to current Swedish clinical practice. The model covers the use of HAL cystoscopy in the operating room to guide transurethral resection of the bladder (TURB) in all patients. HAL cystoscopy as an adjunct to WLC allows for more accurate and complete resection of tumours compared with WLC alone, and the model assumed a consequent reduction in recurrence of 40%. RESULTS: The model projects that compared with WLC alone, use of HAL cystoscopy in the first TURB of all patients and for all TURBs due to recurrence in the first year after diagnosis, leads to a reduction of 23 cystectomies and 180 TURBs in a population of 2032 newly diagnosed bladder cancer patients. Avoidance of these procedures would result in a saving of SEK1 321 716 to the Swedish health service. CONCLUSION: HAL cystoscopy, used as an adjunct to white light in guiding TURB in NMIBC patients, may result in reduction of invasive, time-intensive and high-cost procedures such as cystectomy and TURB, compared with WLC alone.