Comparative Study on Medical Artificial Intelligence Algorithm Assisted and Conventional Imaging Examination Methods

Comparative Study on the Clinical Efficacy of Medical Artificial Intelligence Algorithm Assisted and Conventional Imaging Examination Methods for Chest Wall Tumor Surgery

Chest wall tumors are one of the important diseases in thoracic surgery, and surgery remains the main method for treating this disease in clinical practice. The surgery for chest wall tumors requires extensive resection, and more importantly, precise resection. If the resection range is insufficient, it is easy to cause tumor recurrence and metastasis, which affects the patient's survival; If the resection range is too large, it will cause damage to the chest wall structure, affecting the patient's postoperative recovery and quality of life. At present, the determination of the surgical resection range mainly relies on the experience of the surgeon and the results of imaging examinations. Even if experienced surgeons still have multiple imaging examination results, there are still clinical difficulties of insufficient or excessive resection. Medical artificial intelligence is the in-depth application of artificial intelligence technology in the field of medicine. By processing and analyzing massive amounts of medical data, it can accurately locate tumors and optimize surgical plans. Therefore, it is proposed to compare the clinical effects of surgical resection of chest wall tumors using medical artificial intelligence algorithms and conventional imaging examination methods, in order to understand whether it can achieve more accurate tumor resection.

Study Overview

• Status: Not yet recruiting
• Conditions: Chest Wall Tumor

Detailed Description

Chest wall tumor is one of the important diseases in thoracic surgery, which can be classified as benign or malignant according to the nature of the tumor. At present, surgery is still the main treatment for this disease, even for a few malignant tumors that are sensitive to radiotherapy and chemotherapy, further surgical treatment is still needed after initial internal medicine treatment. Due to the different nature and location of tumors, the scope of surgical resection may vary greatly. Clinical studies have found that surgery for chest wall tumors requires the adoption of appropriate treatment plans based on the pathological results of the tumor, the location of tumor growth, the degree of local invasion of the tumor, and the presence of metastasis at the time of tumor detection. Further research has found that surgical resection of primary chest wall malignant tumors should be performed under careful planning, as most patients only have one chance of cure, and reoperation after tumor recurrence or surgical failure becomes very difficult. Moreover, even if these patients undergo reoperation, their prognosis is poor.

Malignant chest wall tumors require extensive resection to ensure the thoroughness of the surgery. However, the extensive chest wall defect formed after extensive resection can lead to the destruction of the integrity and stability of the chest wall. If not handled properly, it can cause chest wall softening, abnormal breathing, and acute respiratory failure in the early postoperative period, affecting the therapeutic effect of the surgery; However, in the late stage after surgery, chest wall deformities, pulmonary hernias, chronic respiratory dysfunction, and even scoliosis may occur, which can affect the quality of life.

Therefore, for chest wall tumor surgery, it is not only required to achieve thorough enlargement and resection, but also to consider precise resection to preserve the normal structure of the chest wall as much as possible to avoid adverse consequences. For some well-defined malignant tumors of the chest wall, determining the surgical resection margin is relatively simple. However, for other malignant tumors of the chest wall, such as those with invasive growth, those that recur after the first surgery, or those that recur locally after radiotherapy and chemotherapy, it is often difficult to determine the appropriate surgical resection range and resection margin in clinical practice. At this point, the surgical plan can only be determined and formulated based on the clinical experience of the surgeon and traditional imaging examination results before surgery, which has great uncertainty and increases the complexity and difficulty of the surgery.

Medical artificial intelligence is the in-depth application of artificial intelligence technology in the field of medicine. It integrates knowledge from multiple disciplines such as computer science, data science, and biomedical engineering, aiming to improve the efficiency, accuracy, and personalization of medical services by simulating human intelligent behavior. MedAI provides intelligent services for physicians to assist in diagnosis [5-7], recommend treatment methods, and monitor patients by processing and analyzing massive amounts of medical data, thereby optimizing the allocation of medical resources and improving the patient's medical experience.

At present, MedAI is mainly focused on screening lung nodules, determining the nature of lung nodules, and providing three-dimensional simulation imaging of lung nodules as a reference for surgical methods in the field of thoracic surgery. However, there have been no reports on its application in the clinical treatment of chest wall tumors. Therefore, we plan to conduct research in this area to broaden the application of MedAI in general thoracic surgery and provide better medical quality services for chest wall tumor patients, especially those with malignant chest wall tumors.

In the field of surgical planning for chest wall tumors, conventional imaging methods such as CT and MRI can provide basic anatomical information, but they have limitations. Doctors need to manually analyze two-dimensional images, which makes it difficult to accurately construct the three-dimensional spatial relationship between tumors and complex chest wall structures (such as ribs, blood vessels, and nerves), especially when the tumor boundaries are blurred and adhered to surrounding tissues, which can easily lead to surgical planning deviations and affect the integrity and safety of tumor resection The significant advantages of medical artificial intelligence algorithms in this regard lie in precise tumor localization, multidimensional data analysis, and surgical plan optimization.

• Study Type: Observational
• Enrollment (Estimated): 100

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients with benign or malignant chest wall tumors require surgical treatment ,meanwhile they have good overall condition and no signs of metastasis,

Description

Inclusion Criteria:

18-70 years old, male or female not limited Anesthesia ASA score I-II Malignant tumor of soft tissue in the chest Malignant tumors of ribs, rib cartilage, and sternum Tumors with uncertain or unknown properties of ribs, rib cartilage, and sternum Giant benign tumors of ribs, rib cartilage, and sternum The preoperative examination results indicate that the tumor has not undergone distant metastasis Willing to participate in the research and sign the informed consent form

Exclusion Criteria:

Patients with distant metastasis detected during preoperative examination Inoperable tumor During the examination, it was discovered that the patient had another type of malignant tumor present ECOG 4 Suffering from active or chronic fungal/bacterial/viral infections History of allergy to anesthesia related drugs Heart and lung dysfunction, liver and kidney dysfunction, inability to tolerate surgery Patients with mental disorders who are unable to cooperate with treatment

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Medical Artificial Intelligence Algorithm Assistance Group; Before surgery, medical artificial intelligence algorithms are used to outline the resection range of chest wall tumors, while during the actual surgical process, the resection range is determined by intraoperative frozen pathology. Ultimately, the value of medical artificial intelligence algorithm assistance will be evaluated based on paraffin pathology of surgical margins.
Routine imaging examination group; Before surgery, conventional imaging methods are used to outline the resection range of chest wall tumors, while during the actual surgical process, the resection range is determined by intraoperative frozen pathology. The value of routine imaging examination was ultimately evaluated by paraffin pathology of the surgical margin.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Pathological results of surgical margins for chest wall tumors	Pathological diagnosis of residual tissue cells after tumor resection	3 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Surgical margins planned by medical artificial intelligence	Surgical resection edges and ranges delineated with the assistance of medical artificial intelligence algorithms	3 years
Surgical margins planned using conventional imaging techniques	The surgical resection edge and range delineated by conventional imaging examination methods	3 years
recurrence-free survival， RFS	The time from surgery to the earliest evidence of recurrence	3 years
Disease-free survival，DFS	The time at which the patient dies due to disease recurrence or progression after surgery	3 years

Collaborators and Investigators

• Sponsor: Shanghai Jiao Tong University Affiliated Sixth People's Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): July 1, 2025
• Primary Completion (Estimated): July 30, 2025
• Study Completion (Estimated): December 31, 2028

Study Registration Dates

• First Submitted: June 19, 2025
• First Submitted That Met QC Criteria: June 19, 2025
• First Posted (Actual): June 27, 2025

Study Record Updates

• Last Update Posted (Actual): June 27, 2025
• Last Update Submitted That Met QC Criteria: June 19, 2025
• Last Verified: June 1, 2025

More Information

Terms related to this study

• Other Study ID Numbers: 20250521

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No