Simple Summary Breast cancer is still the most common cancer in the female population and is the second leading cause of cancer death in women. Although only 6% of breast cancers have metastatic spread at onset, metastases remain the first cause of death. An artificial intelligence approach could be a valuable noninvasive predictor of the risk of distant metastasis. The purpose of this study is to determine the role of a Deep Learning model approach based on a convolutional neural network in predicting the risk of distant metastasis in patients with breast cancer using dynamic Contrast-Enhanced 3T-MRI images. Background: The incidence of breast cancer metastasis has decreased over the years. However, 20-30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model to predict the risk of distant metastasis using 3T-MRI DCE sequences (Dynamic Contrast-Enhanced). Methods: A total of 157 breast cancer patients who underwent staging 3T-MRI examinations from January 2011 to July 2022 were retrospectively examined. Patient data, tumor histological and MRI characteristics, and clinical and imaging follow-up examinations of up to 7 years were collected. Of the 157 MRI examinations, 39/157 patients (40 lesions) had distant metastases, while 118/157 patients (120 lesions) were negative for distant metastases (control group). We analyzed the role of the Deep Learning technique using a single variable size bounding box (SVB) option and employed a Voxel Based (VB) NET CNN model. The CNN performance was evaluated in terms of accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Results: The VB-NET model obtained a sensitivity, specificity, accuracy, and AUC of 52.50%, 80.51%, 73.42%, and 68.56%, respectively. A significant correlation was found between the risk of distant metastasis and tumor size, and the expression of PgR and HER2. Conclusions: We demonstrated a currently insufficient ability of the Deep Learning approach in predicting a distant metastasis status in patients with BC using CNNs.
3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study
Santucci D.;Faiella E.;Cordelli E.;Soda P.;Iannello G.;
2023-01-01
Abstract
Simple Summary Breast cancer is still the most common cancer in the female population and is the second leading cause of cancer death in women. Although only 6% of breast cancers have metastatic spread at onset, metastases remain the first cause of death. An artificial intelligence approach could be a valuable noninvasive predictor of the risk of distant metastasis. The purpose of this study is to determine the role of a Deep Learning model approach based on a convolutional neural network in predicting the risk of distant metastasis in patients with breast cancer using dynamic Contrast-Enhanced 3T-MRI images. Background: The incidence of breast cancer metastasis has decreased over the years. However, 20-30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model to predict the risk of distant metastasis using 3T-MRI DCE sequences (Dynamic Contrast-Enhanced). Methods: A total of 157 breast cancer patients who underwent staging 3T-MRI examinations from January 2011 to July 2022 were retrospectively examined. Patient data, tumor histological and MRI characteristics, and clinical and imaging follow-up examinations of up to 7 years were collected. Of the 157 MRI examinations, 39/157 patients (40 lesions) had distant metastases, while 118/157 patients (120 lesions) were negative for distant metastases (control group). We analyzed the role of the Deep Learning technique using a single variable size bounding box (SVB) option and employed a Voxel Based (VB) NET CNN model. The CNN performance was evaluated in terms of accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Results: The VB-NET model obtained a sensitivity, specificity, accuracy, and AUC of 52.50%, 80.51%, 73.42%, and 68.56%, respectively. A significant correlation was found between the risk of distant metastasis and tumor size, and the expression of PgR and HER2. Conclusions: We demonstrated a currently insufficient ability of the Deep Learning approach in predicting a distant metastasis status in patients with BC using CNNs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
