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Your search keyword '"Imai, Kentaro"' showing total 10 results
Start Over Author "Imai, Kentaro" Publisher american society of clinical oncology
10 results on '"Imai, Kentaro"'

3. Coformulation of pembrolizumab and vibostolimab in patients (pts) with docetaxel-pretreated metastatic castration-resistant prostate cancer (mCRPC): KEYNOTE-365 cohort G.

Author

Fong, Peter C.C., Kwiatkowski, Mariusz, Mosca, Alessandra, Perez Valderrama, Begona, Huang, Yi-Hsiu, Zedan, Ahmed H., Kuc, Kamil, Wiechno, Pawel, Laguerre, Brigitte, Gonzalez-Billalabeitia, Enrique, Osipov, Mikhail, Sener Dede, Didem, Goh, Jeffrey, Daugaard, Gedske, Zhu, Pengfei, Imai, Kentaro, liu, yingjie, and Arranz Arija, Jose Angel

Published
2024
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4. Pembrolizumab (pembro) plus lenvatinib (lenva) in patients (pts) with docetaxel-pretreated, metastatic castration-resistant prostate cancer (mCRPC): KEYNOTE-365 cohort E.

Author

Augustin, Marinela, Laguerre, Brigitte, Baldini, Capucine, Zedan, Ahmed H., Gonzalez-Billalabeitia, Enrique, Fong, Peter C.C., Zukov, Ruslan, Hammerer, Peter, Prentice, Mark, Shore, Neal D., Necchi, Andrea, Todenhöfer, Tilman, Kessler, Elizabeth R., Köse, Fatih, Gurney, Howard, Perez Valderrama, Begona, Zhu, Pengfei, Imai, Kentaro, liu, yingjie, and McDermott, Raymond S.

Published
2024
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5. Evaluation of a Natural Language Processing Model to Identify and Characterize Patients in the United States With High-Risk Non–Muscle-Invasive Bladder Cancer.

Author

Narayan, Vikram M., Siolas, Despina, Meadows, Eric S., Turzhitsky, Vladimir, Sillah, Arthur, Imai, Kentaro, McMurry, Andrew J., and Li, Haojie

Subjects
NON-muscle invasive bladder cancer, NATURAL language processing, MEDICAL terminology, CANCER diagnosis, ELECTRONIC health records, DEEP learning
Abstract

PURPOSE: Treatment of non–muscle-invasive bladder cancer (NMIBC) is guided by risk stratification using clinical and pathologic criteria. This study aimed to develop a natural language processing (NLP) model for identifying patients with high-risk NMIBC retrospectively from unstructured electronic medical records (EMRs) and to apply the model to describe patient and tumor characteristics. METHODS: We used three independent EMR-derived data sets including adult patients with a bladder cancer diagnosis in 2011-2020 for NLP model development and training (n = 140), validation (n = 697), and application for the retrospective cohort analysis (n = 4,402). Deep learning methods were used to train NLP recognition of medical chart terminology to identify seven high-risk NMIBC criteria; model performance was assessed using the F1 score, weighted across features. An algorithm was then used to classify each patient as high-risk NMIBC (yes/no). Manually reviewed records served as the gold standard. RESULTS: The F1 scores after model training were >0.7 for all but one uncommon feature (prostatic urethral involvement). The highest area under the receiver operating curves (AUC) was observed for Ta (0.897) and T1 (0.897); the lowest AUC was for carcinoma in situ (CIS; 0.617). For high-risk NMIBC classification, positive predictive value was 79.4%, negative predictive value was 93.2%, and false-positive rate was 8.9%. Sensitivity and specificity were 83.7% and 91.1%, respectively. Of 748 patients manually confirmed as having high-risk NMIBC, 196 (26%) had CIS (of whom 19% also had T1 and 23% also had Ta disease); 552 tumors (74%) had no associated CIS. CONCLUSION: The NLP model, combined with a rule-based algorithm, identified high-risk NMIBC with good performance and will enable future work to study real-world treatment patterns and clinical outcomes for high-risk NMIBC. [ABSTRACT FROM AUTHOR]

Published
2023
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7. Adjuvant pembrolizumab (pembro) for renal cell carcinoma (RCC) across UCLA Integrated Staging System (UISS) risk groups and disease stage: Subgroup analyses from the KEYNOTE-564 study.

Author

Choueiri, Toni K., Tomczak, Piotr, Park, Se Hoon, Venugopal, Balaji, Ferguson, Tom, Symeonides, Stefan N., Hajek, Jaroslav, Chang, Yen-Hwa, Lee, Jae-Lyun, Sarwar, Naveed, Thiery-Vuillemin, Antoine, Gross-Goupil, Marine, Mahave, Mauricio, Haas, Naomi B., Sawrycki, Piotr, Zhang, Tian, Xu, Lei, Imai, Kentaro, Poehlein, Christian Heinrich, and Powles, Thomas

Published
2023
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