Model Predicts Skin Cancer Surgery Defect Size

Researchers developed a new tool to estimate defect size following Mohs surgery for periocular skin cancers.

Updated on Oct. 30, 2026 in Cancer

Model Predicts Skin Cancer Surgery Defect Size

Researchers at UT Southwestern Medical Center unveiled a predictive model in October 2026 to assist with surgical planning for periocular basosquamous carcinoma. The study used linear regression and ImageJ software to estimate post-Mohs surgical defect sizes.

Why it matters

Developing precise preoperative models helps surgeons better anticipate the scale of tissue removal required for complex eye-area skin cancers. This approach aims to improve surgical planning and patient outcomes for rare and aggressive malignancies.

The mean defect expansion was 211.6 mm² for basosquamous carcinoma cases, significantly higher than the 108.4 mm² observed for basal cell carcinoma and 53.1 mm² for squamous cell carcinoma. Preoperative lesion area demonstrated an R-squared value of 0.91.

The players

UT Southwestern Medical Center

This Dallas-based institution is a leading academic medical center where the research team conducted their analysis.

The details

The analysis reviewed cases treated between 2010 and 2025 to create a predictive model for surgeons. By measuring tumor area with ImageJ software, researchers found that preoperative dimensions effectively indicate the final post-Mohs defect size.

The study reviewed skin cancer cases treated between 2010 and 2025.
The findings were presented at academic meetings held October 8-12, 2026.

Health Landscape

This research builds on the established use of Mohs micrographic surgery by introducing quantitative digital modeling to surgical planning. It marks a shift toward data-driven surgical projections for complex facial skin cancers that were previously reliant on clinical intuition.

Patients undergoing surgery for periocular skin cancers in Dallas may benefit from improved surgical precision and reduced uncertainty regarding the final scope of their procedure. This research provides surgeons with better tools to plan reconstructive efforts following tumor removal.

The takeaway

Using digital imaging software to quantify lesion size can provide surgeons with highly accurate expectations for tissue removal. This technique helps bridge the gap between initial diagnosis and the reality of complex surgical reconstruction.