{
  "abstract": "The tumor immune microenvironment in colorectal cancer has emerged as a major determinant of patient outcome, with tumor-infiltrating lymphocytes, particularly CD8⁺ T cells, mediating antitumor immunity. Standardized density-based metrics such as the Immunoscore have demonstrated prognostic value but cannot capture the spatial interactions among cells that underlie immune–tumor biology. We introduce a deep learning framework that integrates two complementary axes of analysis: Cu-Cyto, a deep learning–based image cytometry platform that detects and classifies approximately twenty cell types from standard immunohistochemistry-stained whole-slide images, using a bit-pattern kernel-filtering algorithm to prevent multi-counting and an off-target labeling strategy for precise nuclear-center localization; and the Co-Localization Index, which converts the classification probabilities and coordinates produced by Cu-Cyto into a single quantitative measure of co-localization between two or three cell types. We illustrate this framework through CD103⁺CD8⁺ tissue-resident memory–like T cells in rectal cancer treated with neoadjuvant chemoradiotherapy: their stromal but not intratumoral density independently predicts relapse-free survival. This divergence, which compartment-aware density alone cannot explain, motivates a metric that captures within-compartment spatial relationships. The Co-Localization Index extends naturally to three-cell interactions, providing a quantitative readout of the tri-cellular biology among CD103⁺CD8⁺ T cells, tumor cells, and stromal components. Prospective validation in independent rectal-cancer cohorts, addressing watch-and-wait organ preservation and adjuvant chemotherapy decisions, will determine whether this spatial-cellular framework translates into routine clinical decision support with potential extension to other solid tumor contexts.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 Japan",
    "Division of Biophysics, Department of Health Sciences, Graduate School of Medicine, Kobe University, 7-10-2 Tomogaoka, Suma-ku, Kobe, Hyogo 654-0142 Japan",
    "Association of Medical Artificial Intelligence Curation (AMAIC), Sakae Members Office Building, Room 505, 4-16-8 Sakae, Naka-ku, Nagoya, Aichi 460-0008 Japan",
    "Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 Japan",
    "Center for Medical Education and Clinical Training, Faculty of Medicine, Kindai University, 1-14-1 Miharadai, Minami-ku, Sakai, Osaka 590-0197 Japan",
    "Division of Radiation Oncology, Graduate School of Medicine, Kobe University, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 Japan",
    "Department of Gastroenterological Surgery, Hyogo Cancer Center, 13-70 Kitaoji-cho, Akashi, Hyogo 673-8558 Japan"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 23,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kimihiro Yamashita",
      "orcid": "0000-0002-4208-7138"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Toru Nagasaka"
    },
    {
      "affs": [
        0
      ],
      "name": "Tomoki Abe"
    },
    {
      "affs": [
        0
      ],
      "name": "Yukari Adachi"
    },
    {
      "affs": [
        0
      ],
      "name": "Ryota Ito"
    },
    {
      "affs": [
        0
      ],
      "name": "Takaaki Tachibana"
    },
    {
      "affs": [
        3
      ],
      "name": "Toru Takahashi"
    },
    {
      "affs": [
        0
      ],
      "name": "Hiroki Kagiyama"
    },
    {
      "affs": [
        0
      ],
      "name": "Masaki Imai"
    },
    {
      "affs": [
        0
      ],
      "name": "Takao Tsuneki"
    },
    {
      "affs": [
        0
      ],
      "name": "Masayuki Ando"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuna Asano"
    },
    {
      "affs": [
        1
      ],
      "name": "Kumiko Miyashita"
    },
    {
      "affs": [
        0
      ],
      "name": "Yasunori Otowa"
    },
    {
      "affs": [
        0
      ],
      "name": "Naoki Urakawa"
    },
    {
      "affs": [
        0
      ],
      "name": "Hironobu Goto"
    },
    {
      "affs": [
        0
      ],
      "name": "Hiroshi Hasegawa"
    },
    {
      "affs": [
        0
      ],
      "name": "Shingo Kanaji"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Mitsugu Fujita"
    },
    {
      "affs": [
        0
      ],
      "name": "Takeru Matsuda"
    },
    {
      "affs": [
        5
      ],
      "name": "Ryohei Sasaki"
    },
    {
      "affs": [
        3
      ],
      "name": "Takumi Fukumoto"
    },
    {
      "affs": [
        0,
        6
      ],
      "name": "Yoshihiro Kakeji"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ef4np9mep5cnd.bib",
  "cited_by": 0,
  "doi": "10.1007/s10147-026-03182-0",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13710448/",
  "id": "ef4np9mep5cnd",
  "issns": [
    "1341-9625",
    "1437-7772"
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  "landing_url": "https://doi.org/10.1007/s10147-026-03182-0",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Invited Review Article"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
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  "openalex": "W7211969896",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s10147-026-03182-0.pdf",
  "pmcid": "PMC13710448",
  "pmid": "42709377",
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ef4np9mep5cnd.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:25Z",
      "native_id": "PMC13710448",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s10147-026-03182-0"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "From cell counts to cellular interactions: Cu-Cyto and the co-localization index as a spatial framework for the tumor immune microenvironment of rectal cancer",
  "title_text": "From cell counts to cellular interactions: Cu-Cyto and the co-localization index as a spatial framework for the tumor immune microenvironment of rectal cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:25Z",
  "url": "https://research.draconitic.com/paper/ef4np9mep5cnd",
  "venue": "International Journal of Clinical Oncology",
  "versions": []
}