Cancer, Neoadjuvant Therapy
Conditions
Keywords
Artificial Intelligence, Multimodal Fusion, Therapy Prediction, Magnetic Resonance Imaging, Digital Pathology
Brief summary
This study is a multi-center observational study without interventions, including the construction of an AI predictive model, with retrospective and prospective testing. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant treatment, with complete clinical and pathological data. The study plans to enroll 130 patients from our center, collecting corresponding imaging images, and gathering clinical and genomic data to build and internally validate a multimodal AI model. The model's generalization and robustness will be tested to explore the association between multimodal data and the efficacy of neoadjuvant treatment for bladder cancer. The aim is to assist clinicians in predicting and evaluating the efficacy of neoadjuvant treatment for bladder cancer, with the goal of improving patient diagnosis, treatment outcomes, and prognosis.
Detailed description
Bladder cancer is one of the most common malignancies of the genitourinary system worldwide. For muscle - invasive bladder cancer amenable to radical resection, the standard treatment is neoadjuvant therapy combined with radical cystectomy, with neoadjuvant therapy playing a crucial role. Currently, numerous studies have shown that cisplatin - based neoadjuvant chemotherapy can downstage tumors, reduce the risk of mortality in bladder cancer patients, improve survival rates, and enhance prognosis. Other treatment approaches such as neoadjuvant immunotherapy, targeted therapy, and combination therapies are also under investigation. However, responses to neoadjuvant therapy vary among bladder cancer patients, with some not achieving the desired therapeutic goals. Therefore, accurately predicting participants' response to treatment can provide an important reference for personalized and precise treatment of bladder cancer. In recent years, as advancements in computational power and data storage capacity, artificial intelligence (AI) has been widely applied in the field of digital diagnostics. AI technologies can extract and integrate a large number of features from multimodal data such as pathology, imaging, and clinical records, enabling precise disease diagnosis, prognosis assessment, and treatment prediction. In the field of tumor treatment prediction, multimodal AI technologies have achieved numerous breakthroughs, developing efficacy prediction models for tumors such as rectal and breast cancer based on imaging and pathological data, and validating the models' generalization capabilities through external validation. Therefore, the investigators plan to construct and validate a "Bladder Cancer Neoadjuvant Treatment Efficacy Prediction Model" based on multimodal data (including MRI images, digital pathology images, and clinical records) of bladder cancer patients, and develop an AI-assisted prediction software for neoadjuvant treatment efficacy in bladder cancer. The study will adopt a combined retrospective and prospective data collection design to ensure sufficient sample size and model robustness. This study plans to enroll a total of 550 patients, including 500 retrospective cases and 50 prospective cases. Based on sample size calculation formulas and preliminary study results, the minimum sample size required for model development is estimated to be 220 cases. Considering imaging quality control, internal validation, and external validation needs, we plan to retrospectively enroll 500 cases from both the local center and external institutions. Due to the relative rarity of bladder cancer patients receiving neoadjuvant therapy, 50 prospective cases planned to verify the model's performance and clinical applicability.
Interventions
DIAGNOSTIC_TESTArtificial intelligence (AI)-based predictive model
Collect magnetic resonance imaging and pathological slides of resected tumor of the enrolled patients. Analyze the data using the AI model to generate predictive results (sensitive or insensitive to the neoadjavant therapy). No intervention to patients would be performed in this diagnostic test study.
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Bladder occupying lesions, with histopathological confirmation of bladder cancer after resection. * Planned neoadjuvant therapy and radical cystectomy.
Exclusion criteria
* Patients who have not undergone standard bladder imaging examinations or have missing imaging or pathological data. * Patients who have received local treatments (such as interventional embolization) or systemic treatments (such as radiotherapy, chemotherapy, immunotherapy, or targeted therapy). * Poor quality of imaging or pathological images.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC (Area Under the Receiver Operating Characteristic Curve) | For each enrolled patient, the AI model's prediction results will be generated within several days after neoadjuvant therapy. The AUC of the model will be evaluated upon study completion, an average of 3 years. | A comprehensive metric reflecting the overall discriminative ability of the AI model, which integrates the model's sensitivity and specificity across all possible threshold values. It quantifies the probability that the model will correctly rank a randomly selected therapy-sensitive patient higher than a randomly selected therapy-insensitive patient. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| sensitivity | For each enrolled patient, the predictive results of AI model will be obtained in several days after neoadjuvant therapy, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year. | the number of correctly diagnosed positive patient (sensitive to therapy), to be divided by the number of patients in total. |
| specificity | For each enrolled patient, the predictive results of AI model will be obtained in several days after neoadjuvant therapy, and the specificity of the AI model will be evaluated through study completion, an average of 3 year. | the number of correctly diagnosed negative patients (therapy insensitive), to be divided by the number of negative patients in total. |
Countries
China
Outcome results
None listed