Deep Learning for Musculoskeletal Complications in Breast Cancer

AI-Powered Deep Learning Models for Prospective Prediction of Musculoskeletal Complications After Breast Cancer Surgery: Focus on Lymphedema, Axillary Web Syndrome, Neuropathy, and Pain

Survival after breast cancer has increased due to early diagnosis and advances in treatment methods. Musculoskeletal problems related to cancer and its treatment constitute a significant part of the daily practice of physiatrists and rehabilitation specialists involved in oncological rehabilitation.

Lymphedema can occur at any stage of a patient's life following breast cancer. Patients with breast cancer-related lymphedema require lifelong treatment, and as the stage of lymphedema progresses, response to therapy decreases. Advanced stages of lymphedema negatively affect functional status, and patients experience difficulties in performing activities of daily living.

Axillary web syndrome (AWS) is characterized by a taut cord extending from the axilla to the volar surface of the wrist, typically appearing within the first 8 weeks postoperatively. AWS can complicate the administration of radiotherapy. Shoulder dysfunction may occur independently or in association with AWS. In particular, scapular dyskinesis developing after mastectomy can lead to secondary shoulder conditions such as rotator cuff syndrome or adhesive capsulitis, which are commonly observed in these patients.

Peripheral neuropathy is frequently seen in patients receiving chemotherapy, adversely affecting daily life and sometimes preventing continuation of treatment. Other complications related to chemotherapy and radiotherapy include cardiotoxicity, pulmonary toxicity, fatigue, osteoporosis, and cognitive impairment.

There are also specific painful syndromes that may occur after breast cancer, including post-mastectomy pain syndrome, phantom breast pain, and musculoskeletal symptoms associated with aromatase inhibitors. All these conditions can significantly impair daily functioning and even hinder continuation of cancer treatment. Therefore, predicting these complications and implementing or developing preventive interventions is crucial.

If it is possible to predict the early development of lymphedema, axillary web syndrome, peripheral neuropathy, and painful syndromes after breast cancer, early intervention may prevent progression. This study is designed to develop and validate a predictive model using deep learning methods to determine the risk of these complications in patients undergoing breast cancer surgery. Among deep learning architectures, ResNet50, AlexNet, GoogleNet, and UNet, which have been widely used in recent studies, are planned to be implemented.

Additionally, based on the results of this study, a risk calculation program will be developed, allowing clinicians to input baseline patient data and calculate the individual patient's risk for each complication prior to treatment. No specific risk is expected in the study.

Study Overview

• Status: Recruiting
• Conditions: Axillary Web Syndrome; Shoulder Adhesive Capsulitis; Postmastectomy Lymphedema Syndrome; Osteoporosis Secondary; Breast Cancer Surgery Pain
• Intervention / Treatment: Diagnostic test: physical examination

• Study Type: Observational
• Enrollment (Estimated): 133

Contacts and Locations

• Study Contact: Name: Başak Mansız Kaplan; Phone Number: +905358582176; Email: basakmansiz@hotmail.com

Study Locations

• Turkey (Türkiye)
  • Ankara, Turkey (Türkiye)
    • Recruiting
    • Ankara Etlik City Hospital

Participation Criteria

Eligibility Criteria

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

• Sampling Method: Non-Probability Sample

Study Population

The study will include adult women (over 18 years of age) who are scheduled to undergo surgery for unilateral breast cancer. Patients with a history of bilateral breast cancer, male breast cancer, inability to comply with follow-up visits, or those who are children, pregnant, postpartum, breastfeeding, in intensive care, or with impaired consciousness, as well as legally incapacitated individuals, will be excluded

Description

Inclusion Criteria:

Female sex Age ≥18 years Scheduled for surgery due to unilateral breast cancer

Exclusion Criteria:

Inability to comply with follow-up visits Bilateral breast cancer Male breast cancer Children (<18 years) Pregnant women Postpartum women Breastfeeding women Individuals in intensive care Impaired consciousness Legally incapacitated individuals

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
shoulder range of motion	Shoulder range of motion will be measured in all directions using a goniometer before treatment and during follow-up visits	shoulder range of motion will be measured in all directions using a goniometer before treatment and during follow-up visits. (0, month 1, month 3, month 6)

Collaborators and Investigators

• Sponsor: Ankara Etlik City Hospital

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2025
• Primary Completion (Estimated): July 1, 2026
• Study Completion (Estimated): January 1, 2027

Study Registration Dates

• First Submitted: November 15, 2025
• First Submitted That Met QC Criteria: November 15, 2025
• First Posted (Actual): November 19, 2025

Study Record Updates

• Last Update Posted (Actual): March 31, 2026
• Last Update Submitted That Met QC Criteria: March 30, 2026
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Joint Diseases; Bursitis; Diagnostic Techniques and Procedures; Diagnosis; Physical Examination
• Other Study ID Numbers: AEŞH-EK-2025-145

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: I do not consider sharing IPD ethically appropriate

Drug and device information, study documents

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