Dedicated Breast PET-Based Deep Learning Radiomics for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer.
Tianhao Zeng, Yilin He, Teng Zhang, Caiyue Ren, Jun Xu, Jingyi Cheng, Wenlong Ming
Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
7 authors.
Tianhao ZengJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing 210044, China.
Yilin HeDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of Radiation Oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201315, China.
Teng ZhangJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing 210044, China.ORCID 0000-0002-0978-2876
Caiyue RenDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of Radiation Oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201315, China.
Jun XuJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing 210044, China.ORCID 0000-0001-5315-8811
Jingyi ChengDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of Radiation Oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201315, China.
Wenlong MingJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing 210044, China.
Funding
Anhui Key Laboratory of Computational Medicine and Intelligent Health AHCM2024Z001Jiangsu Provincial Department of Science and Technology's major project on frontier-leading basic research in technology No. BK2023200National Key R&D Program of China 2023YFC3402800National Natural Science Foundation of China Nos. 82441029, 62171230, 62101365, 92159301, 62301263, 62301265, 62302228, 82302291, 82302352, 62401272, 82402447, 62501292, 62502223the Talent Start-up Funding Project of Nanjing University of Information Science and Technology 2024r090
6 · The paper itself
Abstract
PubMed holds no abstract for this paper.
Indexed as
dedicated breast PETdeep learning radiomicsHER2-positive breast cancerneoadjuvant chemotherapypathologic complete response prediction
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.
Dedicated Breast PET-Based Deep Learning Radiomics for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer. · full record | Socratic