A Machine Learning Model to Predict the Histology of Retroperitoneal Lymph Node Dissection Specimens.

Journal: Anticancer research
PMID: 38677742

Abstract

BACKGROUND/AIM: While post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) benefits patients with teratoma or viable germ cell tumors (GCT), it becomes overtreatment if necrosis is detected in PC-RPLND specimens. Serum microRNA-371a-3p correctly predicts residual viable GCT with 100% sensitivity; however, prediction of residual teratoma in PC-RPLND specimens using current modalities remains difficult. Therefore, we developed a machine learning model using CT imaging and clinical variables to predict the presence of residual teratoma in PC-RPLND specimens.

Authors

  • Satoshi Nitta
    Department of Urology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
  • Takahiro Kojima
    Department of Urology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
  • Masanobu Gido
    Department of Intelligent Functional Systems, Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan.
  • Shota Nakagawa
    Rist, Inc., Impact HUB Tokyo, 2-11-3 Meguro, Meguro-ku, Tokyo, 153-0063, Japan.
  • Hideki Kakeya
    Department of Intelligent Functional Systems, Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan.
  • Shuya Kandori
    Department of Urology, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
  • Takashi Kawahara
    Department of Urology and Renal Transplantation, Yokohama City University Medical Center.
  • Bryan J Mathis
    International Medical Center, University of Tsukuba Affiliated Hospital, Tsukuba, Japan.
  • Koji Kawai
    Department of Urology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
  • Hiromitsu Negoro
    Department of Urology, University of Tsukuba, Tsukuba, Ibaraki, Japan.
  • Hiroyuki Nishiyama
    Department of Urology, University of Tsukuba Hospital, Tsukuba, Japan.

Keywords

External Resources

Popular Topics
Recent Journals