Development and External Validation of an Improved Version of the Diagnostic Model for Opportunistic Screening of Malignant Esophageal Lesions.
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SUBMITTER: Liu Z
PROVIDER: S-EPMC9737355 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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Development and External Validation of an Improved Version of the Diagnostic Model for Opportunistic Screening of Malignant Esophageal Lesions.
Liu Zhen Z Zheng Hongchen H Liu Mengfei M He Yujie Y Chen Yun Y Ji Ping P Fang Zhengyu Z Xiao Ping P Li Fenglei F Guo Chuanhai C Yin Weihua W Pan Yaqi Y He Zhonghu Z Ke Yang Y
Cancers 20221130 23
We aimed to develop an improved version of the diagnostic model predicting the risk of malignant esophageal lesions in opportunistic screening and validate it in external populations. The development set involved 10,595 outpatients receiving endoscopy from a hospital in Hua County, a high-risk region for esophageal squamous cell carcinoma in northern China. Validation set A enrolled 9453 outpatients receiving endoscopy in a non-high-risk region in southern China. Validation set B involved 17,511 ...[more]
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Project description:ImportanceRetinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening.ObjectiveTo evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP.Design, setting, and participantsThis diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023.ExposuresAn imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine.Main outcomes and measuresThe area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels.ResultsThe prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis.Conclusions and relevanceWhere and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.
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Project description:BackgroundThis is a retrospective cohort study from a single center of Chest Medical District of Nanjing Brain Hospital Affiliated to Nanjing Medical University, Jiangsu Province, China. It was aim to evaluate the diagnostic value of radial endobronchial ultrasound (R-EBUS) combination with rapid on-site evaluation (ROSE) guided transbronchial lung biopsy (TBLB) for peripheral pulmonary lesions in patients with emphysema.MethodsAll 170 patients who underwent PPLs with emphysema received an R-EBUS examination with or without the ROSE procedure, and the diagnostic yield, safety, and possible factors influencing diagnosis were analyzed between the two groups by the SPSS 25.0 software.ResultsThe pooled and benign diagnostic yields were not different in the two groups (P = 0.224, 0.924), but the diagnostic yield of malignant PPLs was significantly higher in the group with ROSE than the group without ROSE (P = 0.042). The sensitivity of ROSE was 79.10%, the specificity, 91.67%, the positive predictive value, 98.15%, and the negative predictive value, 84.62%. The diagnostic accuracy, was 95.52%. In the group of R-EBUS + ROSE, the procedural time and the number of times of biopsy or brushing were both significantly reduced (all P<0.05). The incidence of pneumothorax (1.20%) and bleeding (10.84%) in the group of R-EBUS + ROSE were also less than those in the group of R-EBUS (P<0.05). The lesion's diameter ≥ 2 cm, the distance between the pleura and the lesion ≥ 2 cm, the positive air bronchograms sign, the location of the ultrasound probe within the lesion, and the even echo with clear margin feature of lesion ultrasonic image, these factors are possibly relevant to a higher diagnostic yield. The diagnostic yield of PPLs those were adjacent to emphysema were lower than those PPLs which were away from emphysema (P = 0.048) in the group without ROSE, however, in the group of R-EBUS + ROSE, there was no such difference whether the lesion is adjacent to emphysema or not (P = 0.236).ConclusionOur study found that the combination of R-EBUS and ROSE during bronchoscopy procedure was a safe and effective modality to improve diagnostic yield of PPLs with emphysema, especially for malignant PPLs. The distance between the pleura and the lesion ≥ 2 cm, the positive air bronchograms sign, the location of the ultrasound probe within the lesion, and the even echo with clear margin feature of lesion ultrasonic image, these factors possibly indicated a higher diagnostic yield. Those lesions' position is adjacent to emphysema may reduce diagnostic yield but ROSE may make up for this deficiency.
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Project description:Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer. Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV1), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data. Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55-0.76) and 0.70 (95% CI 0.58-0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host. Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV1 that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.
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