Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT-Reference-Cited by-同舟云学术

Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT

Published:2023-08-29 Issue:17 Volume:149 Page:15469-15478
ISSN:0171-5216
Container-title:Journal of Cancer Research and Clinical Oncology
language:en
Short-container-title:J Cancer Res Clin Oncol

Author:

Li Lei,Zhou Xinglu,Cui Wenju,Li Yingci,Liu Tianyi,Yuan Gang,Peng Yunsong,Zheng Jian

Funder

the Harbin Medical University Cancer Hospital Haiyan Fund Youth Funding Project

the Scientific Research Project of Heilongjiang Health Commission

the Guizhou Provincial People's Hospital Talent Fund (Yunsong Peng) under Grant Hospital Talent Project

the Suzhou science and technology plan project

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Oncology,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00432-023-05329-2.pdf

Reference41 articles.

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2. Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19:57. https://doi.org/10.1186/s13058-017-0846-1

3. Cui W, Peng Y, Yuan G, Cao W, Cao Y, Lu Z, Ni X, Yan Z, Zheng J (2022) FMRNet: a fused network of multiple tumoral regions for breast tumor classification with ultrasound images. Med Phys 49:144–157. https://doi.org/10.1002/mp.15341

4. Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169

5. He QH, Feng JJ, Lv FJ, Jiang Q, Xiao MZ (2023) Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions. Insights Imaging 14:6. https://doi.org/10.1186/s13244-022-01349-7

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