Artificial Intelligence based breast thermography using radiomic feature extraction versus conventional manual interpretation of breast thermograms in the prediction of breast cancer: a multi-reader study

ObjectiveTo evaluate the performance of Thermalytix, an artificial intelligence-enhanced breast thermal imaging analysis software, against unaided manual interpretation of thermographic images. MethodsIn this multi-reader study, thermal imaging data of 258 symptomatic participants from a previous clinical trial were used. These images were independently manually interpreted by 3 senior trained breast radiologists. The same images were independently evaluated by Thermalytix, which uses sophisticated machine learning analysis of thermal/ vascular radiomic parameters to generate a risk score predictive of cancer . The results of manual interpretation and Thermalytix were compared with reference standard based on standard of care (combination of mammography, ultrasound and histopathology), to determine sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and area under receiver operating characteristic curves (AUROC). ResultsThermalytix obtained showed a sensitivity and specificity of 95.2% (90% confidence interval (CI), 90.0- 100.0) and 66.7% (CI 60.1-73.3); the NPV and PPV were 97.7% (CI 95.2%-100.3%) and 58.3% (CI 48.5%-68.2%). The (sensitivity, specificity, NPV, PPV) obtained by Reader 1, Reader 2 and Reader 3 were (60.3%, 81.5%, 51.4%, 86.4%), (74.6%,50.8%, 86.1%, 32.9%) and (71.4%, 63.8%, 87.2%, 38.5%), respectively. The AUROC of Thermalytix was 0.85, 13.7% greater than manual interpretation. ConclusionThermalytix demonstrated good clinical performance with 25% higher accuracy than manual interpretation of thermal images. Thermalytix may alleviate the known subjectivity in thennography thereby improving its performance.