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Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data
Computer Vision and Pattern Recognition · Submitted 7 Jul 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub}
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Renal cell carcinoma represents a significant global health challenge with a low survival rate. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. | contribution 0.97 | 0.60 | 0.04 | asserted 0.83 |
| s2 | The aim is to facilitate the identification of patients requiring urgent treatment. | contribution 0.40 | 0.13 | 0.03 | asserted 0.90 |
| s3 | The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. | method 0.95 | 0.83 | 0.03 | asserted 1.00 |
| s4 | The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. | method 1.00 | 0.83 | 0.03 | asserted 1.00 |
| s5 | A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. | method 1.00 | 0.93 | 0.02 | asserted 1.00 |
| s6 | A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. | method 1.00 | 0.90 | 0.03 | asserted 0.99 |
| s7 | Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. | method 0.99 | 0.96 | 0.02 | asserted 0.98 |
| s8 | Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. | result 0.87 | 0.94 | 0.72 | asserted 1.00 |
| s9 | The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. | result 0.99 | 0.97 | 0.17 | hedged possibility 0.58 |
| s10 | The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. | interpretation 0.57 | 0.93 | 0.67 | asserted 0.83 |
| s11 | This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. | interpretation 1.00 | 0.21 | 0.10 | hedged possibility 1.00 |
| s12 | The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub} | contribution 0.85 | 0.67 | 0.02 | asserted 0.99 |
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The paper
Original abstract
Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub}
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