We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients.
- Authors
Park, Hyun-Soo; Lee, Kwang-sig; Seo, Bo-Kyoung; Kim, Eun-Sil; Cho, Kyu-Ran; Woo, Ok-Hee; Song, Sung-Eun; Lee, Ji-Young; Cha, Jaehyung
- Abstract
Simple Summary: Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine. This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.
- Subjects
BREAST tumor risk factors; DIGITAL image processing; PREDICTIVE tests; PHYSICS; EPIDERMAL growth factor receptors; MACHINE learning; RANDOM forest algorithms; CANCER patients; TREATMENT failure; ESTROGEN receptors; COMPUTED tomography; PREDICTION models; TUMOR markers; RECEIVER operating characteristic curves; BREAST tumors; LONGITUDINAL method; ALGORITHMS; TUMOR grading; ANTIGENS; PROGESTERONE receptors; PERFUSION
- Publication
Cancers, 2021, Vol 13, Issue 23, p6013
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers13236013