Chest Wall Tumor

Chest wall reconstruction following tumor or infection-related resections remains a challenging aspect of thoracic surgery, requiring restoration of structural stability and preservation of respiratory mechanics. While polymethyl methacrylate (PMMA) bone cement has long been used for rigid reconstruction, its limitations-including high cost, rigidity, infection risk, and interference with normal respiratory motion-pose challenges in resource-constrained settings. Twisted stainless steel wires offer a low-cost, flexible alternative that allows dynamic chest wall movement and easier adaptability in low- and middle-income countries such as Pakistan. To compare postoperative outcomes, complications, and cost-effectiveness of chest wall reconstruction using twisted stainless steel wires versus PMMA bone cement over a two-year period (January 2025 - December 2026). This prospective cohort study was conducted in the Department of Thoracic Surgery, Services Hospital, Lahore, a high-volume tertiary care and referral center. Patients undergoing chest wall reconstruction following resection for tumors, infections, or trauma were enrolled and divided into two groups based on the reconstruction technique used: Group A (twisted steel wires) and Group B (PMMA bone cement). Parameters assessed included postoperative pain (VAS scores), respiratory function, chest wall stability, complications (infection, wound dehiscence, prosthesis exposure), duration of hospital stay, readmission rate, and cost of reconstruction. Data were analyzed to compare clinical and functional outcomes between both cohorts.

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.

Chest wall tumors are one of the important diseases in thoracic surgery, and surgery is still the main treatment for this disease in clinical practice. Surgery for chest wall tumors requires extensive resection. However, the extensive chest wall defect formed after extensive resection can lead to the destruction of the integrity and stability of the chest wall, thus requiring chest wall reconstruction. The reconstruction of the chest wall after resection has always been a difficult point in chest wall tumor surgery, and the reconstruction of the chest wall bone is a key point in chest wall reconstruction. The goals of successful chest wall bone reconstruction include restoring chest wall rigidity, minimizing chest wall deformities, maintaining lung mechanics, and protecting thoracic organs. At present, traditional reconstruction methods are based on simulating and restoring the anatomical structure of the thoracic spine. For certain special areas of chest wall defects (such as posterior rib and paraspinal tumors), it is difficult to perform residual fixation; At the same time, traditional reconstruction methods suffer from insufficient stability after reconstruction, such as loosening, detachment, displacement, and even fracture. Therefore, it is necessary to design new chest wall reconstruction methods to meet clinical needs.

Chest wall tumors should be completely resection as much as possible while malignant chest wall tumors should be extensively resection. If not completely resection, it will recur in the short time and affect the patient's survival. At present, the surgical resection range mainly relies on preoperative imaging examination and the experience of the surgeon. It lacks precise guidance. This can easily lead to incomplete resection. In addition, the reconstruction materials required for reconstruct the excised chest wall defection are often generated in a standardized manner, lacking intraoperative adjustability. To address this clinical issue, we plan to carry out the research on the application of artificial Intelligence (AI) assisted chest wall tumor resection combined with personalized 3D preformed chest wall defection reconstruction system in chest wall tumor surgery.

This project focuses on patients with chest wall tumors who are expected to have a maximum intraoperative anterolateral chest wall defect diameter of 5-10 cm and require chest wall reconstruction with rib implants. It aims to evaluate the efficacy and safety of biodegradable 3D-printed implants in chest wall reconstruction surgery, providing a superior reconstruction strategy for patients with tumor-induced chest wall defects.

This is a Phase II single center, open-label, non-randomized study in patients with locally recurrent, inoperable, and/or metastatic inflammatory breast cancer with lymphangitic spread to the chest wall. Patients will be treated with pembrolizumab administered as an intravenous infusion at 200 mg in 21-day treatment cycles and oral cyclophosphamide (CTX) 50 mg per day in metronomic administration as a 21 days cycle Forty-six patients will be required for the study. Key inclusion criteria are PDL1 (≥1%) positive and/or tumor infiltrating lymphocyte positive (≥1%) locally advanced "chest wall" breast cancer (with or without distant metastases), who have been treated with chemotherapy or radiation therapy may be eligible for this study. Patients with cutaneous metastases only (with or without evidence of primary tumor) are eligible for the study. Key exclusion criteria included prior anti PD1 or anti CTLA-4 or other immune pathway-targeted therapy. Patients with autoimmune diseases and/or receiving drugs who interfere with the immune system will not be eligible.