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ARXIV:2604.09197 · MEDICAL AI · SUBMITTED 13 APR · 20:23 UTC · FRESHNESS STALE
ARXIV:2604.09197MEDICAL AISUBMITTED 13 APR · 20:23 UTCFRESHNESS STALEFrancesca Fati · Felipe Coutinho · Marika Reinius · Marina Rosanu · Gabriel Funingana · Luigi De Vitis · +22 at arXiv
A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams.
Opportunity summary
Pain A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams. High-grade serous ovarian carcinoma (HGSOC) is characterized by…
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams.
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10.48550/arXiv.2604.09197A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams.
Abstract
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuvant chemotherapy (NACT) followed by delayed primary surgery is commonly employed in patients unsuitable for primary cytoreduction. The Chemotherapy Response Score (CRS) is a validated histopathological biomarker of response to NACT, but it is only available postoperatively. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support adjunct to inform multidisciplinary team (MDT) discussions regarding expected treatment response. Methods. We proposed a 2.5D multimodal deep learning framework that processes lesion-dense omental slices using a pre-trained Vision Transformer encoder and integrates the resulting visual representations with clinical variables through an intermediate fusion module to predict CRS. Results. Our multimodal model, integrating imaging and clinical data, achieved a ROC-AUC of 0.95 alongside 95% accuracy and 80% precision on the internal test cohort (IEO, n=41 patients). On the external test set (OV04, n=70 patients), it achieved a ROC-AUC of 0.68, alongside 67% accuracy and 75% precision. Conclusion. These preliminary results demonstrate the feasibility of transformer-based deep learning for preoperative prediction of CRS in HGSOC using routine clinical data and CT imaging. As an investigational, pre-treatment decision-support tool, this approach may assist MDT discussions by providing early, non-invasive estimates of treatment response.
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Dimensions overall score 7.0
PROBLEM
A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological...
METHOD
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support adjunct to inform multidisciplinary...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support adjunct to inform multidisciplinary team (MDT) discussions regarding expected treatment response. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A multimodal deep learning framework using Vision Transformers can predict chemotherapy response in ovarian cancer from CT scans, acting as a decision-support tool for multidisciplinary teams.
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Medical AI
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Commercial read
7.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
next verification path
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stale
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passport absent
stale
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stale
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Technical feasibility
partial
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