Radiomics of magnetic resonance imaging for assessment of pathological complete response to neoadjuvant therapy and long-term survival in breast cancer
Breast cancer (BC) is the most common malignant disease. The use of neoadjuvant drug therapy increases the likelihood of achieving a complete pathomorphological response (pCR), leads to an increase in resectability and ablation; helps to determine the sensitivity of tumor cells to chemopreventive ag...
| Published in: | Исследования и практика в медицине |
|---|---|
| Main Authors: | N. V. Petrova, G. G. Karmazanovsky, E. V. Kondratyev, A. Yu. Popov, M. V. Rostovtsev, N. Yu. Germanovich, D. V. Kalinin |
| Format: | Article |
| Language: | Russian |
| Published: |
QUASAR, LLC
2023-09-01
|
| Subjects: | |
| Online Access: | https://www.rpmj.ru/rpmj/article/view/901 |
Similar Items
Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients
by: Yimiao Yu, et al.
Published: (2024-01-01)
by: Yimiao Yu, et al.
Published: (2024-01-01)
Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer
by: Xue Li, et al.
Published: (2024-07-01)
by: Xue Li, et al.
Published: (2024-07-01)
Radiomics and radiogenomics in intrahepatic cholangiocarcinoma
by: A. D. Smirnova, et al.
Published: (2024-03-01)
by: A. D. Smirnova, et al.
Published: (2024-03-01)
Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery
by: Kaiyuan Li, et al.
Published: (2023-05-01)
by: Kaiyuan Li, et al.
Published: (2023-05-01)
Predicting Pathological Response of Neoadjuvant Conversion Therapy for Hepatocellular Carcinoma Patients Using CT-Based Radiomics Model
by: Wen H, et al.
Published: (2024-11-01)
by: Wen H, et al.
Published: (2024-11-01)
Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer
by: Yun Zhu, et al.
Published: (2025-06-01)
by: Yun Zhu, et al.
Published: (2025-06-01)
Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer
by: Yuwen Liang, et al.
Published: (2025-06-01)
by: Yuwen Liang, et al.
Published: (2025-06-01)
Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue
by: Guangying Zheng, et al.
Published: (2024-01-01)
by: Guangying Zheng, et al.
Published: (2024-01-01)
Radiomics nomogram combined with clinical factors for predicting pathological complete response in resectable esophageal squamous cell carcinoma
by: Zihao Lu, et al.
Published: (2024-10-01)
by: Zihao Lu, et al.
Published: (2024-10-01)
A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma
by: Yu Yang, et al.
Published: (2024-04-01)
by: Yu Yang, et al.
Published: (2024-04-01)
Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer
by: Nan Xu, et al.
Published: (2024-01-01)
by: Nan Xu, et al.
Published: (2024-01-01)
Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning
by: Jiaxuan Peng, et al.
Published: (2023-04-01)
by: Jiaxuan Peng, et al.
Published: (2023-04-01)
Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer
by: Jianjing Liu, et al.
Published: (2024-07-01)
by: Jianjing Liu, et al.
Published: (2024-07-01)
MRI-PATHOLOGICAL PARALLELS WITH THE COMPLETE TUMOR RESPONSE TO NEOADJUVANT CHEMORADIATION TREATMENT OF RECTAL CANCER
by: T. P. Berezoskaya, et al.
Published: (2019-06-01)
by: T. P. Berezoskaya, et al.
Published: (2019-06-01)
CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients
by: Weibo Kong, et al.
Published: (2024-12-01)
by: Weibo Kong, et al.
Published: (2024-12-01)
Radiomics for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Prospective Observational Trial
by: Liming Shi, et al.
Published: (2023-05-01)
by: Liming Shi, et al.
Published: (2023-05-01)
CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients
by: Liyuan Fan, et al.
Published: (2024-06-01)
by: Liyuan Fan, et al.
Published: (2024-06-01)
Predictive value of background parenchymal enhancement on breast magnetic resonance imaging for pathological tumor response to neoadjuvant chemotherapy in breast cancers: a systematic review
by: Xue Li, et al.
Published: (2024-03-01)
by: Xue Li, et al.
Published: (2024-03-01)
A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer
by: Wenjing Peng, et al.
Published: (2023-08-01)
by: Wenjing Peng, et al.
Published: (2023-08-01)
Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective studyResearch in context
by: YuHong Huang, et al.
Published: (2023-04-01)
by: YuHong Huang, et al.
Published: (2023-04-01)
Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study
by: Wendong Qu, et al.
Published: (2024-03-01)
by: Wendong Qu, et al.
Published: (2024-03-01)
Prediction of Breast Cancer Response to Neoadjuvant Therapy with Machine Learning: A Clinical, MRI-Qualitative, and Radiomics Approach
by: Rami Hajri, et al.
Published: (2025-07-01)
by: Rami Hajri, et al.
Published: (2025-07-01)
A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients
by: Shasha Liu, et al.
Published: (2023-01-01)
by: Shasha Liu, et al.
Published: (2023-01-01)
Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI
by: Seunghyun Lee, et al.
Published: (2021-10-01)
by: Seunghyun Lee, et al.
Published: (2021-10-01)
Combined morphology and radiomics of intravoxel incoherent movement as a predictive model for the pathologic complete response before neoadjuvant chemotherapy in patients with breast cancer
by: Yunyan Zheng, et al.
Published: (2025-02-01)
by: Yunyan Zheng, et al.
Published: (2025-02-01)
Predictive ability of ultrasound radiomics features combined with miRNA-34a expression levels for pathological complete response in breast cancer patients receiving neoadjuvant chemotherapy
by: ZHANG Weina, ZHONG Lichang, SHI Lin, LAI Jinyu, CHEN Jiehuan, GU Liping
Published: (2025-07-01)
by: ZHANG Weina, ZHONG Lichang, SHI Lin, LAI Jinyu, CHEN Jiehuan, GU Liping
Published: (2025-07-01)
CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study
by: Meng Zhang, et al.
Published: (2024-01-01)
by: Meng Zhang, et al.
Published: (2024-01-01)
Accuracy of core biopsy image-guided post-neoadjuvant chemotherapy breast to predict pathologic complete response
by: P. V. Krivorotko, et al.
Published: (2022-12-01)
by: P. V. Krivorotko, et al.
Published: (2022-12-01)
Ability of MRI Breast to Predict Pathologic Complete Response Following Neoadjuvant Systemic Therapy in Patients with Breast Cancer
by: Anu L. Joy, et al.
by: Anu L. Joy, et al.
CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer
by: Guanchao Ye, et al.
Published: (2024-06-01)
by: Guanchao Ye, et al.
Published: (2024-06-01)
Pathological complete response following neoadjuvant chemotherapy for locally advanced intrahepatic cholangiocarcinoma
by: Yoshitaka Shimamaki, et al.
Published: (2024-02-01)
by: Yoshitaka Shimamaki, et al.
Published: (2024-02-01)
Complete pathological response following neoadjuvant chemoradiotherapy in locally advanced colorectal carcinoma
by: Ozana Miličević, et al.
Published: (2022-01-01)
by: Ozana Miličević, et al.
Published: (2022-01-01)
Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer
by: Bikash Panthi, et al.
Published: (2023-10-01)
by: Bikash Panthi, et al.
Published: (2023-10-01)
A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors
by: Wei-Xiang Qi, et al.
Published: (2024-01-01)
by: Wei-Xiang Qi, et al.
Published: (2024-01-01)
The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer
by: Jia Wang, et al.
Published: (2024-05-01)
by: Jia Wang, et al.
Published: (2024-05-01)
Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
by: Jiamin Guo, et al.
Published: (2024-06-01)
by: Jiamin Guo, et al.
Published: (2024-06-01)
Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort
by: Siyao Du, et al.
Published: (2025-04-01)
by: Siyao Du, et al.
Published: (2025-04-01)
The Tumor–Fat Interface Volume of Breast Cancer on Pretreatment MRI Is Associated with a Pathologic Response to Neoadjuvant Chemotherapy
by: Hwan-ho Cho, et al.
Published: (2020-11-01)
by: Hwan-ho Cho, et al.
Published: (2020-11-01)
Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram
by: Lu Zhang, et al.
Published: (2024-03-01)
by: Lu Zhang, et al.
Published: (2024-03-01)
PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning
by: Witali Aswolinskiy, et al.
Published: (2023-11-01)
by: Witali Aswolinskiy, et al.
Published: (2023-11-01)
Similar Items
-
Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients
by: Yimiao Yu, et al.
Published: (2024-01-01) -
Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer
by: Xue Li, et al.
Published: (2024-07-01) -
Radiomics and radiogenomics in intrahepatic cholangiocarcinoma
by: A. D. Smirnova, et al.
Published: (2024-03-01) -
Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery
by: Kaiyuan Li, et al.
Published: (2023-05-01) -
Predicting Pathological Response of Neoadjuvant Conversion Therapy for Hepatocellular Carcinoma Patients Using CT-Based Radiomics Model
by: Wen H, et al.
Published: (2024-11-01)
