Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors

Sun, Di; Hadjiiski, Lubomir; Gormley, John; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Alva, Ajjai; Gulani, Vikas; Zhou, Chuan

doi:10.3390/cancers15174372

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…In a previous study, a combined model incorporating DL features, radiomics features, and clinical factors was built to predict survival after radical surgery for bladder cancer 27 . However, the study’s patient cohort was relatively small and included only a single medical center.…”

Section: Discussionmentioning

confidence: 99%

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study

Wei,

Xv,

Liu

et al. 2024

International Journal of Surgery

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Background: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a deep learning (DL) model based on preoperative CT for predicting post-cystectomy overall survival in patients with MIBC. Methods: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation and external validation sets. A deep learning model incorporated the convolutional block attention module (CBAM) was built for predicting overall survival using preoperative CT images. We assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. Results: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P<0.01). The DLRN further improved the performance, with a C-index of 0.713 (95%CI: 0.627-0.798) in the internal validation set and 0.685 (95%CI: 0.586-0.765) in external validation set, respectively. Conclusions: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

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Section: Discussionmentioning

confidence: 99%

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study

Wei,

Xv,

Liu

et al. 2024

International Journal of Surgery

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Development of a Fair Machine Learning Model to Predict Survival after Radical Cystectomy for Bladder Cancer (Preprint)

Carbunaru,

Neshatvar,

et al. 2024

Preprint

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BACKGROUND Prediction models based on machine learning (ML) methods are being increasingly developed and adopted in healthcare. However, these models may be prone to bias and be considered unfair if they demonstrate variable performance in population subgroups. An unfair model is of particular concern in bladder cancer where disparities have been identified in sex and race subgroups. OBJECTIVE In this study, we developed a ML model to predict survival after radical cystectomy for bladder cancer, and evaluated for potential model bias in sex and race subgroups. We then compared algorithm unfairness mitigation techniques to improve model fairness. METHODS We trained and compared various ML classification algorithms to predict 5-year survival after radical cystectomy using the National Cancer Database. The primary model performance metric was the F1 score. The primary metric for model fairness was the equalized odds ratio (eOR). We compared three algorithm unfairness mitigating techniques to improve the eOR. RESULTS We identified 16,481 patients; 23.1% were female; 91.5% were ‘White’, 5.0% were ‘Black’, 2.3% were ‘Hispanic’, and 1.2% were ‘Asian’. The 5-year mortality rate was 74.6%. The best naive model was XGBoost which had an F1 score of 0.860 and eOR of 0.619. All unfairness mitigation techniques increased the eOR, with correlation remover showing the highest increase and resulting in a final eOR of 0.750. This mitigated model had F1 scores of 0.86, 0.904, and 0.824 in the full, Black male, and Asian female test sets, respectively. CONCLUSIONS Our ML model predicting survival after radical cystectomy exhibited bias across sex and race subgroups. By employing algorithm unfairness mitigation techniques, we improved algorithmic fairness as measured by the eOR.Our study highlights the role of evaluating for model bias but also actively mitigating such disparities to ensure equitable healthcare delivery. We also deploy the first online fair ML model predicting survival after radical cystectomy at https://nayanlab.shinyapps.io/fair_cystectomy_survival/.

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Outcome Prediction Using Multi-Modal Information: Integrating Large Language Model-Extracted Clinical Information and Image Analysis

Sun,

Hadjiiski,

Gormley

et al. 2024

Cancers

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Survival prediction post-cystectomy is essential for the follow-up care of bladder cancer patients. This study aimed to evaluate artificial intelligence (AI)-large language models (LLMs) for extracting clinical information and improving image analysis, with an initial application involving predicting five-year survival rates of patients after radical cystectomy for bladder cancer. Data were retrospectively collected from medical records and CT urograms (CTUs) of bladder cancer patients between 2001 and 2020. Of 781 patients, 163 underwent chemotherapy, had pre- and post-chemotherapy CTUs, underwent radical cystectomy, and had an available post-surgery five-year survival follow-up. Five AI-LLMs (Dolly-v2, Vicuna-13b, Llama-2.0-13b, GPT-3.5, and GPT-4.0) were used to extract clinical descriptors from each patient’s medical records. As a reference standard, clinical descriptors were also extracted manually. Radiomics and deep learning descriptors were extracted from CTU images. The developed multi-modal predictive model, CRD, was based on the clinical (C), radiomics (R), and deep learning (D) descriptors. The LLM retrieval accuracy was assessed. The performances of the survival predictive models were evaluated using AUC and Kaplan–Meier analysis. For the 163 patients (mean age 64 ± 9 years; M:F 131:32), the LLMs achieved extraction accuracies of 74%~87% (Dolly), 76%~83% (Vicuna), 82%~93% (Llama), 85%~91% (GPT-3.5), and 94%~97% (GPT-4.0). For a test dataset of 64 patients, the CRD model achieved AUCs of 0.89 ± 0.04 (manually extracted information), 0.87 ± 0.05 (Dolly), 0.83 ± 0.06~0.84 ± 0.05 (Vicuna), 0.81 ± 0.06~0.86 ± 0.05 (Llama), 0.85 ± 0.05~0.88 ± 0.05 (GPT-3.5), and 0.87 ± 0.05~0.88 ± 0.05 (GPT-4.0). This study demonstrates the use of LLM model-extracted clinical information, in conjunction with imaging analysis, to improve the prediction of clinical outcomes, with bladder cancer as an initial example.

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Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors

Cited by 3 publications

References 34 publications

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study

Development of a Fair Machine Learning Model to Predict Survival after Radical Cystectomy for Bladder Cancer (Preprint)

Outcome Prediction Using Multi-Modal Information: Integrating Large Language Model-Extracted Clinical Information and Image Analysis

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