Thomas Ren1, Renee Cattell2, Hongyi Duanmu3, Pauline Huang1, Haifang Li1, Rami Vanguri4, Michael Z Liu5, Sachin Jambawalikar5, Richard Ha5, Fusheng Wang6, Jules Cohen1, Clifford Bernstein1, Lev Bangiyev1, Timothy Q Duong7. 1. Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY. 2. Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Biomedical Engineering. 3. Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Computer Science, Stony Brook University, Stony Brook, NY. 4. Department of Radiology, Columbia University Medical Center, New York, NY; Data Science Institute, Columbia University, New York, NY. 5. Department of Radiology, Columbia University Medical Center, New York, NY. 6. Department of Computer Science, Stony Brook University, Stony Brook, NY; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY. 7. Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY. Electronic address: tim.duong@stonybrookmedicine.edu.
Abstract
BACKGROUND: Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) data were those from the peak contrast dynamic image from 1.5 Tesla MRI scanners at the pre-neoadjuvant chemotherapy stage. Data consisted of 66 abnormal nodes from 38 patients and 193 normal nodes from 61 patients. Abnormal nodes were those determined by expert radiologist based on 18Fluorodeoxyglucose positron emission tomography images. Normal nodes were those with negative diagnosis of breast cancer. The convolutional neural network consisted of 5 convolutional layers with filters from 16 to 128. Receiver operating characteristic analysis was performed to evaluate prediction performance. For comparison, an expert radiologist also scored the same nodes as normal or abnormal. RESULTS: The convolutional neural network model yielded a specificity of 79.3% ± 5.1%, sensitivity of 92.1% ± 2.9%, positive predictive value of 76.9% ± 4.0%, negative predictive value of 93.3% ± 1.9%, accuracy of 84.8% ± 2.4%, and receiver operating characteristic area under the curve of 0.91 ± 0.02 for the validation data set. These results compared favorably with scoring by radiologists (accuracy of 78%). CONCLUSION: The results are encouraging and suggest that this approach may prove useful for classifying lymph node status on MRI in clinical settings in patients with breast cancer, although additional studies are needed before routine clinical use can be realized. This approach has the potential to ultimately be a noninvasive alternative to lymph node biopsy.
BACKGROUND: Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) data were those from the peak contrast dynamic image from 1.5 Tesla MRI scanners at the pre-neoadjuvant chemotherapy stage. Data consisted of 66 abnormal nodes from 38 patients and 193 normal nodes from 61 patients. Abnormal nodes were those determined by expert radiologist based on 18Fluorodeoxyglucose positron emission tomography images. Normal nodes were those with negative diagnosis of breast cancer. The convolutional neural network consisted of 5 convolutional layers with filters from 16 to 128. Receiver operating characteristic analysis was performed to evaluate prediction performance. For comparison, an expert radiologist also scored the same nodes as normal or abnormal. RESULTS: The convolutional neural network model yielded a specificity of 79.3% ± 5.1%, sensitivity of 92.1% ± 2.9%, positive predictive value of 76.9% ± 4.0%, negative predictive value of 93.3% ± 1.9%, accuracy of 84.8% ± 2.4%, and receiver operating characteristic area under the curve of 0.91 ± 0.02 for the validation data set. These results compared favorably with scoring by radiologists (accuracy of 78%). CONCLUSION: The results are encouraging and suggest that this approach may prove useful for classifying lymph node status on MRI in clinical settings in patients with breast cancer, although additional studies are needed before routine clinical use can be realized. This approach has the potential to ultimately be a noninvasive alternative to lymph node biopsy.
Authors: Frederik Abel; Anna Landsmann; Patryk Hejduk; Carlotta Ruppert; Karol Borkowski; Alexander Ciritsis; Cristina Rossi; Andreas Boss Journal: Diagnostics (Basel) Date: 2022-05-29
Authors: Renee Cattell; Jia Ying; Lan Lei; Jie Ding; Shenglan Chen; Mario Serrano Sosa; Chuan Huang Journal: Vis Comput Ind Biomed Art Date: 2022-03-07