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Predicting Papillary Renal Cell Carcinoma Prognosis Using Integrative Analysis of Histopathological Images and Genomic Data

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Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13920))

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Abstract

Renal cell carcinoma (RCC) is a common malignant tumor of the adult kidney, with the papillary subtype (pRCC) as the second most frequent. There is a need to improve evaluative criteria for pRCC due to overlapping diagnostic characteristics in RCC subtypes. To create a better prognostic model for pRCC, we proposed an integration of morphologic and genomic features. Matched images and genomic data from The Cancer Genome Atlas were used. Image features were extracted using CellProfiler, and prognostic image features were selected using least absolute shrinkage and selection operator and support vector machine algorithms. Eigengene modules were identified using weighted gene co-expression network analysis. Risk groups based on prognostic features were significantly distinct (p < 0.05) according to Kaplan-Meier analysis and log-rank test results. We used two image features and nine eigengene modules to construct a model with the Random Survival Forest method, measuring 11-, 16-, and 20-month areas under the curve (AUC) of a time-dependent receiver operating curve. The integrative model (AUCs: 0.877, 0.769, and 0.811) outperformed models trained with eigengenes alone (AUCs: 0.75, 0.733, and 0.785) and morphological features alone (AUCs: 0.593, 0.523, 0.603). This suggests that an integrative prognostic model based on histopathological images and genomic features could significantly improve survival prediction for pRCC patients and assist in clinical decision-making.

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Author information

Authors and Affiliations

  1. Department of Natural Sciences, University of St. La Salle, Bacolod, Philippines

    Shaira L. Kee, Michael Aaron G. Sy & Myles Joshua T. Tan

  2. Department of Electronics Engineering, University of St. La Salle, Bacolod, Philippines

    Michael Aaron G. Sy & Myles Joshua T. Tan

  3. J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA

    Samuel P. Border, Nicholas J. Lucarelli & Pinaki Sarder

  4. Division of Nephrology, Hypertension and Renal Transplantation – Quantitative Health Section, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA

    Samuel P. Border, Nicholas J. Lucarelli, Akshita Gupta, Pinaki Sarder & Myles Joshua T. Tan

  5. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Akshita Gupta

  6. Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA

    Pinaki Sarder & Myles Joshua T. Tan

  7. Medical Education Unit, School of Medicine, Southwestern University - PHINMA, Cebu, Philippines

    Marvin C. Masalunga

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  1. Shaira L. Kee
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  2. Michael Aaron G. Sy
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  3. Samuel P. Border
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  5. Akshita Gupta
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  7. Marvin C. Masalunga
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Correspondence to Myles Joshua T. Tan .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas Ruiz

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Kee, S.L. et al. (2023). Predicting Papillary Renal Cell Carcinoma Prognosis Using Integrative Analysis of Histopathological Images and Genomic Data. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-34960-7_15

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