Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

Journal: Medical physics
PMID: 39369684

Abstract

BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early pCR prediction.

Authors

  • Bowen Jing
    Department of Computer Science, Stanford University, Stanford, California, USA.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Erich Schmitz
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Shanshan Tang
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Yunxiang Li
    The Key Lab of RF Circuits and Systems of Ministry of Education, Microelectronics CAD Center, Hangzhou Dianzi University, Hangzhou, 310018, China.
  • You Zhang
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.

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