Clinical trial NCT06589154: The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis
Official title: The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Healthy People
Benign Prostatic Hyperplasia
Prostate Cancer
Lead sponsor: Shanghai Changzheng Hospital
Sponsor class: OTHER
Collaborators: name: Ningbo No. 1 Hospital
name: The First Affiliated Hospital of Soochow University
name: The First Affiliated Hospital of Guangzhou Medical University
name: Jiangsu Provincial People's Hospital
name: Zhejiang University
name: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
name: West China Hospital
First posted: 2024-09-19
Enrollment: 1000
Sex: MALE
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: People who are required to undergo prostatic or pelvic magnetic resonance (MR) examination

Brief summary: The application of multimodal data fusion in prostate cancer will bring new opportunities to improve diagnostic accuracy and treatment outcomes. By integrating imaging, genomics, pathology, and clinical data, we expect to realize precision medicine for prostate cancer and significantly improve the survival prognosis of patients.

Detailed description: Prostate cancer stands as one of the most prevalent malignancies among men across the globe. Global cancer statistics reveal that the number of new prostate cancer diagnoses rises annually.

In recent years, the advent of multimodal data fusion technology has paved the way for innovative diagnostic and prognostic approaches in prostate cancer. For instance, the synergy of MRI imaging with clinical indicators such as PSA levels and Gleason scores not only refines the staging of prostate cancer but also sheds light on a patient's likely response to treatment and survival outlook.

The exponential growth of artificial intelligence, particularly the broad adoption of deep learning and machine learning algorithms, has emerged as a formidable asset in the realm of multimodal data analysis. By developing an AI-driven multimodal data fusion system, vast medical datasets can be efficiently processed, laying the groundwork for a more nuanced auxiliary diagnostic and therapeutic decision-making framework.

Eligibility criteria: Inclusion Criteria:

1. Male, 18-80 years of age;
2. Patients with normal prostate, prostatic hyperplasia, or prostate cancer who have undergone a prostate or pelvic magnetic resonance (MR) examination;
3. First visit on January 1, 2014 or later.

Exclusion Criteria:

1. Patients with a diagnosis of any other malignancy within the previous 5 years;
2. Patients who have undergone transurethral resection or enucleation of the prostate prior to undergoing imaging;
3. Patients who are not suitable for participation in this clinical trial in the judgment of the investigator;

Patients who meet any of the above criteria may not be enrolled as subjects.

Primary outcomes:
measure: Sensitivity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: Specificity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: ROC value of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
timeFrame: Through completion of study and all data analysis which may take up to one year.
Secondary outcomes:
measure: ROC value of a Prostate Cancer Multimodal Model in Predicting the Pathological Outcomes of Gleason Score Categories (≤6, 7, ≥8) in Men Underwent for Prostate Biopsy
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: Sensitivity of a Prostate Cancer Multimodal Model in Predicting the Pathological Outcomes of Gleason Score Categories (≤6, 7, ≥8) in Men Underwent for Prostate Biopsy
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: Specificity of a Prostate Cancer Multimodal Model in Predicting the Pathological Outcomes of Gleason Score Categories (≤6, 7, ≥8) in Men Underwent for Prostate Biopsy
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: ROC value of a Prostate Cancer Multimodal Model in Predicting Clinically Significant Prostate Cancer (csPCa) in Men Underwent Prostate Biopsy
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: Sensitivity of a Prostate Cancer Multimodal Model in Predicting Clinically Significant Prostate Cancer (csPCa) in Men Underwent Prostate Biopsy
timeFrame: Through completion of study and all data analysis which may take up to one year.
measure: Specificity of a Prostate Cancer Multimodal Model in Predicting Clinically Significant Prostate Cancer (csPCa) in Men Underwent Prostate Biopsy
timeFrame: Through completion of study and all data analysis which may take up to one year.
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Shanghai Changzheng Hospital
state: Shanghai
Study officials:
References: