Dataset Information

Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases.

ABSTRACT: Background:Lung nodules are a diagnostic challenge. Current clinical management of lung nodule patients is inefficient and therefore causes patient misclassification, which increases healthcare expenses. However, a precise and robust lung nodule classifier to minimize discomfort for patients and healthcare costs is still lacking. The aim of the present protocol is to evaluate the effectiveness of using a liquid biopsy classifier to diagnose nodules compared to physician estimates and whether the classifier can reduce the number of unnecessary biopsies in benign cases. Methods:A prospective cohort of 10,560 patients enrolled at 23 clinical centers in China with non-calcified pulmonary nodules, ranging from 0.5 to 3 cm in diameter, indicated by LDCT or CT will be included. After signed consent forms, the participants' pulmonary nodules will be assessed using three evaluation tools: (I) physician cancer probability estimates (II) validated lung nodule risk models, including Mayo Clinic and Veteran's Affairs models (III) ctDNA methylation classifier previously established. Each patient will undergo LDCT/CT follow-ups for 2 to 3 years and their information and one blood sample will be collected at baseline, 3, 6, 12, 24 and 36 months. The primary study outcomes will be the diagnostic accuracy of the methylation classifier in the cohort. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) will be used to compare the diagnostic value of each testing tool in differentiating benign and malignant pulmonary nodules. Discussion:We are conducting an observational study to explore the accuracy of using a ctDNA methylation classifier for incidental lung nodules diagnosis. Trial registration:Clinicaltrials.gov NCT03651986.

SUBMITTER: Liang W

PROVIDER: S-EPMC7653103 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases.

Liang Wenhua W Liu Dan D Li Min M Wang Wei W Qin Zheng Z Zhang Jian J Zhang Yong Y Hu Yang Y Bao Hairong H Xiang Yi Y Wang Bo B Wu Jing J Sun Jianyu J Hu Chengping C Ye Xianwei X Zhang Xiangyan X Xiao Wei W Yun Chunmei C Sun Dejun D Wang Wei W Chang Ning N Zhang Yunhui Y Zhao Jianping J Zhang Xin X Xu Jinfu J Wu Di D Liu Xiaoju X Guo Yubiao Y Zhang Qichuan Q Zhang Wei W Yang Lan L Li Zhanqing Z Zhang Xiaoju X Han Baohui B Tong Zhaohui Z He Jianxing J Qu Jieming J Fan Jian-Bing JB Zhong Nanshan N

Translational lung cancer research 20201001 5

<h4>Background</h4>Lung nodules are a diagnostic challenge. Current clinical management of lung nodule patients is inefficient and therefore causes patient misclassification, which increases healthcare expenses. However, a precise and robust lung nodule classifier to minimize discomfort for patients and healthcare costs is still lacking. The aim of the present protocol is to evaluate the effectiveness of using a liquid biopsy classifier to diagnose nodules compared to physician estimates and whe ...[more]

PMID: 33209621

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Project description:Background: Although most thyroid nodules with indeterminate cytology are benign, in most of the world, surgery remains as the most frequent diagnostic approach. We have previously reported a 10-gene thyroid genetic classifier, which accurately predicts benign thyroid nodules. The assay is a prototype diagnostic kit suitable for reference laboratory testing and could potentially avoid unnecessary diagnostic surgery in patients with indeterminate thyroid cytology. Methods: Classifier performance was tested in two independent, ethnically diverse, prospective multicenter trials (TGCT-1/Chile and TGCT-2/USA). A total of 4061 fine-needle aspirations were collected from 15 institutions, of which 897 (22%) were called indeterminate. The clinical site was blind to the classifier score and the clinical laboratory blind to the pathology report. A matched surgical pathology and valid classifier score was available for 270 samples. Results: Cohorts showed significant differences, including (i) clinical site patient source (academic, 43% and 97% for TGCT-1 and -2, respectively); (ii) ethnic diversity, with a greater proportion of the Hispanic population (40% vs. 3%) for TGCT-1 and a greater proportion of African American (11% vs. 0%) and Asian (10% vs. 1%) populations for TGCT-2; and (iii) tumor size (mean of 1.7 and 2.5 cm for TGCT-1 and -2, respectively). Overall, there were no differences in the histopathological profile between cohorts. Forty-one of 155 and 45 of 115 nodules were malignant (cancer prevalence of 26% and 39% for TGCT-1 and -2, respectively). The classifier predicted 37 of 41 and 41 of 45 malignant nodules, yielding a sensitivity of 90% [95% confidence interval; CI 77-97] and 91% [95% CI 79-98] for TGCT-1 and -2, respectively. One hundred one of 114 and 61 of 70 nodules were correctly predicted as benign, yielding a specificity of 89% [95% CI 82-94] and 87% [95% CI 77-94], respectively. The negative predictive values for TGCT-1 and TGCT-2 were 96% and 94%, respectively, whereas the positive predictive values were 74% and 82%, respectively. The overall accuracy for both cohorts was 89%. Conclusions: Clinical validation of the classifier demonstrates equivalent performance in two independent and ethnically diverse cohorts, accurately predicting benign thyroid nodules that can undergo surveillance as an alternative to diagnostic surgery.

Project description:ImportanceApproximately 20% of fine-needle aspirations (FNA) of thyroid nodules have indeterminate cytology, most frequently Bethesda category III or IV. Diagnostic surgeries can be avoided for these patients if the nodules are reliably diagnosed as benign without surgery.ObjectiveTo determine the diagnostic accuracy of a multigene classifier (GC) test (ThyroSeq v3) for cytologically indeterminate thyroid nodules.Design, setting, and participantsProspective, blinded cohort study conducted at 10 medical centers, with 782 patients with 1013 nodules enrolled. Eligibility criteria were met in 256 patients with 286 nodules; central pathology review was performed on 274 nodules.InterventionsA total of 286 FNA samples from thyroid nodules underwent molecular analysis using the multigene GC (ThyroSeq v3).Main outcomes and measuresThe primary outcome was diagnostic accuracy of the test for thyroid nodules with Bethesda III and IV cytology. The secondary outcome was prediction of cancer by specific genetic alterations in Bethesda III to V nodules.ResultsOf the 286 cytologically indeterminate nodules, 206 (72%) were benign, 69 (24%) malignant, and 11 (4%) noninvasive follicular thyroid neoplasms with papillary-like nuclei (NIFTP). A total of 257 (90%) nodules (154 Bethesda III, 93 Bethesda IV, and 10 Bethesda V) had informative GC analysis, with 61% classified as negative and 39% as positive. In Bethesda III and IV nodules combined, the test demonstrated a 94% (95% CI, 86%-98%) sensitivity and 82% (95% CI, 75%-87%) specificity. With a cancer/NIFTP prevalence of 28%, the negative predictive value (NPV) was 97% (95% CI, 93%-99%) and the positive predictive value (PPV) was 66% (95% CI, 56%-75%). The observed 3% false-negative rate was similar to that of benign cytology, and the missed cancers were all low-risk tumors. Among nodules testing positive, specific groups of genetic alterations had cancer probabilities varying from 59% to 100%.Conclusions and relevanceIn this prospective, blinded, multicenter study, the multigene GC test demonstrated a high sensitivity/NPV and reasonably high specificity/PPV, which may obviate diagnostic surgery in up to 61% of patients with Bethesda III to IV indeterminate nodules, and up to 82% of all benign nodules with indeterminate cytology. Information on specific genetic alterations obtained from FNA may help inform individualized treatment of patients with a positive test result.

Project description:BackgroundThe potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists.ObjectiveThis study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series.MethodsWe used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4's evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician.ResultsThe 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4's evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians' evaluations.ConclusionsGPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

Dataset Information

Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases.

Publications

Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases.

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