{
  "abstract": "Complete cytoreductive surgery remains one of the strongest determinants of outcome in ovarian cancer, yet surgeons still lack rapid tissue-assessment tools that can be repeated throughout an operation. Here we evaluated whether SpiderMass ambient mass spectrometry, combined with machine-learning models, could support ex vivo ovarian tissue typing and exploratory immune microenvironment mapping. A total of 128 ovarian specimens, from 119 patients, were analyzed to train subtype-classification models from fresh-frozen and formalin-fixed paraffin-embedded (FFPE) material, and 24 independent tissues (from 16 patients) were reserved for blinded region-level testing. Initial PCA-LDA models were improved by screening 24 classifiers; Ridge models reached up to 97% 5-fold cross-validation accuracy on the combined cohort. In blinded analyses, the mixed Ridge model produced the fewest errors, although misclassification remained concentrated in underrepresented endometrioid regions. A dual-input network combining SpiderMass spectra with digitized histology improved internal performance to 99% in 5-fold cross-validation and 100% on a small, blinded image-spectrum set, outperforming the molecular-only branch. Model explanation followed by MALDI-MSI cross-checking identified 26 subtype-associated lipids. We then trained a LightGBM cell-state model from immune and epithelial cell spectra and applied it to SpiderMass imaging data. Spatial predictions were broadly concordant with multiplex MALDI-IHC and highlighted subtype-specific differences in immune-cell distribution. In an exploratory analysis of eight high-grade serous carcinoma samples obtained before chemotherapy, longer survivalappeared to be associated with higher lymphocyte scores, higher M1-like macrophage scores and a higher M1/M2 ratio, whereas shorter survival appeared to be associated with higher cancer-cell scores. These data support SpiderMass as a promising ex vivo platform for ovarian cancer typing and hypothesis-generating immunoscoring, while underscoring the need for prospective intraoperative studies, orthogonal biomarker validation, validation in larger cohorts for exploratory immunoscoring and multicenter patient-level external validation before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "Léa Ledoux"
    },
    {
      "name": "Yanis Zirem"
    },
    {
      "name": "Laurine Lagache"
    },
    {
      "name": "Lucas Roussel"
    },
    {
      "name": "Delphine Hudry"
    },
    {
      "name": "Bertrand Meresse"
    },
    {
      "name": "Marie Delbeke"
    },
    {
      "name": "Eric Leblanc"
    },
    {
      "name": "Yves-Marie Robin"
    },
    {
      "name": "Camille Pasquesoone"
    },
    {
      "name": "Delphine Bertin"
    },
    {
      "name": "Fabrice Narducci"
    },
    {
      "name": "Michel Salzet"
    },
    {
      "name": "Isabelle Fournier"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0f6d11vc5vch7.bib",
  "cited_by": 1,
  "doi": "10.1016/j.jare.2026.07.022",
  "fulltext_url": null,
  "id": "0f6d11vc5vch7",
  "issns": [
    "2090-1232"
  ],
  "landing_url": "https://hal.science/hal-05745358v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "1607|Mass Spectrometry Techniques and Applications"
    ]
  ],
  "openalex": "W7167668318",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2090123226005400/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/0f6d11vc5vch7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "hal-05745358",
      "source": "hal",
      "url": "https://hal.science/hal-05745358v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Mass Spectrometry Techniques and Applications",
      "subfield": "physical"
    }
  ],
  "title": "SpiderMass ambient mass spectrometry and multimodal machine learning enable ex vivo ovarian cancer typing and exploratory immunoscoring",
  "title_text": "SpiderMass ambient mass spectrometry and multimodal machine learning enable ex vivo ovarian cancer typing and exploratory immunoscoring",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:33Z",
  "url": "https://research.draconitic.com/paper/0f6d11vc5vch7",
  "venue": "Journal of Advanced Research",
  "versions": []
}