Mei R. Fu, Yao Wang, 2 Chenge Li, 2 Zeyuan Qiu, 3 Deborah Axelrod, 4,5 Amber A. Guth, 4,5 Joan Scagliola, 5 Yvette Conley, 6 Bradley E. Aouizerat, 7 Jeanna M. Qiu, 8 Gary Yu,1 Janet H. Van Cleave, 1 Judith Haber, 1 and Ying Kuen Cheung 9. Mhealth. 2018; 4: 17.

Main findings

• More than 20 symptom features reported by breast cancer survivors have been significantly associated with lympoedema; more importantly the symptom features have discrete biological mechanisms related to inflammation and lymphatic biological mechanism. These symptom features include patient-reported arm swelling, heaviness, tightness, firmness, pain, aching, soreness, tenderness, numbness, stiffness, tingling, burning, limb fatigue, limb weakness, seroma formation, breast swelling, chest wall swelling, limb hotness, blistering, as well as impaired limb mobility in the shoulder, arm, elbow, wrist, and fingers.
• The greater the number of symptoms reported, the greater the limb volume increase.
• Lymphedema symptoms may indicate a critical stage of lymphedema where lymphedema is present but changes in limb volume or limb girth cannot be detected by objective measures.
• Machine learning is a data-driven approach to learn the association between various observable features and the class label from training data.
• Machine learning performs high level computing to design and program explicit algorithms which is not feasible when using a conventional statistical approach. Machine learning is able to construct algorithms that can continue improving the prediction and generate automated knowledge through data-driven predictions or decisions with incoming data. Machine learning is particularly beneficial when there are many relevant features and these features are not independent, which is the case for the lymphedema symptom features.
• A web-based and cross-sectional study was designed to enabled patients’ real-time symptom report, that is, symptoms reported by patients at the time of reporting using a well-established mHealth system, The-OptimalLymph-Flow (TOLF).
• They recruited breast cancer survivors who met the following inclusion criteria: (I) older than 21 years of age; (II) had surgical treatment of lumpectomy or mastectomy as well as lymph node procedures either sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND); and (III) being diagnosed with or treated for lymphedema.
• 355 women provided complete study data.
• Data collection included: demographic and medical information, lymphoedema status, breast cancer and lymphedema symptom experience index.
• mHealth system with machine learning for real-time detection of lymphedema status displayed improved accuracy, sensitivity and specificity. In comparison with conventional statistical procedures, our study further shows that a well-trained artificial; neural network (ANN) classifier can offer accurate evaluation on the patient’s lymphedema status using real-time symptom report by providing 93.75% of a cross-validation accuracy, 95.65% of sensitivity, and 91.03% of specificity. These results provide initial evidence that use of a well-trained classification algorithm to detect lymphedema based on the real-time symptom report using a web-and-mobile-based mHealth system is superior to a standard statistical approach.
• Ultimately, with ongoing data collection and future biomarker data to improve the algorithm from automated machine learning refinement, accurate and real-time detection of lymphedema will enable patients and healthcare providers to accurately monitor their lymphedema risk and seek timely intervention.