Clinical trial NCT05706415: AI Assisted the Diagnosis of Pancreatic Solid Lesions
Official title: Enhanced Deep Learning Model for Diagnosis of Pancreatic Solid Lesions Through Multimodal Clinical Images
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: AI Assist in the Diagnosis of Pancreatic Solid Lesions
Lead sponsor: Changhai Hospital
Sponsor class: OTHER
Collaborators: name: The Third People's Hospital of Chengdu
name: Ruijin Hospital
name: Sun Yat-sen University
name: Wuhan Union Hospital, China
First posted: 2023-01-31
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Observational model: CASE_CROSSOVER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who had pancreatic solid mass will be enrolled in our study.

Brief summary: Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not require surgical treatment, but can be treated with drugs. The common ones are chronic pancreatitis and autoimmune pancreatitis, with a good prognosis. Clinically, the differential diagnosis between them is very difficult. Multi-disciplinary diagnosis and treatment of MDT makes our understanding of pancreatic diseases increasingly rich and in-depth. From disease diagnosis to preoperative evaluation and curative effect evaluation, non-invasive imaging involves almost every link under MDT mode. In view of this, improving the differential diagnosis of pancreatic solid space-occupying lesions on imaging will be more conducive to the diagnosis and treatment under MDT mode, so new technologies such as artificial intelligence should be considered. Our goal is to develop a clinically applicable artificial intelligence system, which uses multiple modes to simulate the routine clinical workflow and assist in the diagnosis of benign and malignant pancreatic solid space-occupying lesions.

Detailed description: The diagnosis of solid pancreatic lesions is challenging, MDT is a very effective method, but it has a certain misdiagnosis rate. This is a multi-center, prospective and observational clinical study. Our goal is to develop a clinically applicable artificial intelligence system. On the one hand, our artificial intelligence based on clinical data+CT imaging images can assist MDT doctors to diagnose the nature of pancreatic space-occupying lesions and reduce misdiagnosis; On the other hand, if a patient needs EUS-FNA puncture, the multimodal artificial intelligence system based on clinical data+CT+EUS developed by us can help MDT doctors understand the nature of pancreatic space-occupying lesions and reduce the probability of misdiagnosis or secondary puncture.

Eligibility criteria: Inclusion Criteria:

* pancreatic solid mass in CT and EUS

Exclusion Criteria:

* insufficient imaging quality of CT or EUS
* endoscopic ultrasound non accessible lesions

Primary outcomes:
measure: Researchers use artificial intelligence (AI) support system to assist in diagnosis of pancreatic solid space-occupying lesions
timeFrame: 2 months
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: CT and EUS
state: Shanghai
city: Shanghai
country: China
facility: CT and EUS
state: Shanghai
Study officials:
References: