Evidence map›Paper›PMID 42523553›Full record

ArticleResearch square2026

Large-Scale Multi-Cancer Detection by Learning Segmentation from Reports.

Pedro R A S Bassi, Xinze Zhou, Wenxuan Li, Szymon Płotka, Jakob Wasserthal, Jieneng Chen, Ibrahim E Hamamci, Sezgin Er, Jakub Prządo, Zheren Zhu and 25 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

35 authors.

Pedro R A S BassiJohns Hopkins University, Baltimore, MD, USA.ORCID 0000-0002-8995-9423
Xinze ZhouJohns Hopkins University, Baltimore, MD, USA.
Wenxuan LiJohns Hopkins University, Baltimore, MD, USA.
Szymon PłotkaJagiellonian University, Kraków, Poland.
Jakob WasserthalUniversity Hospital Basel, Basel, Switzerland.
Jieneng ChenJohns Hopkins University, Baltimore, MD, USA.
Ibrahim E HamamciUniversity of Zurich, Zurich, Switzerland.ORCID 0000-0003-2932-3105
Sezgin ErUniversity of Zurich, Zurich, Switzerland.
Jakub PrządoWarmian-Masurian Cancer Center, Olsztyn, Poland.
Zheren ZhuUniversity of California, Berkeley, CA, USA.
Gorkem DurakNorthwestern University, Illinois, USA.ORCID 0000-0002-1608-1955
Wiktoria RomańczykMedical University of Białystok, Poland.
Yavuz B TaktakIstanbul University, Istanbul, Turkey.
Melih AkanIstanbul Medipol University, Istanbul, Turkey.
Yuhan WangUniversity of California, Santa Cruz, CA, USA.
Scott YeUniversity of California, San Francisco, CA, USA.ORCID 0009-0005-0713-9069
Qi ChenJohns Hopkins University, Baltimore, MD, USA.
Ashwin KumarStanford University, Stanford, CA, USA.ORCID 0009-0002-9859-4275
Lizhou WuShandong Provincial Qianfoshan Hospital, Shandong, China.
Guang ZhangShandong Provincial Qianfoshan Hospital, Shandong, China.
Bjoern MenzeUniversity of Zurich, Zurich, Switzerland.ORCID 0000-0003-4136-5690
Jarosław B ĆwikłaUniversity of Warmia and Mazury, Olsztyn, Poland.
Yuyin ZhouUniversity of California, Santa Cruz, CA, USA.
Frank H MillerNorthwestern University, Illinois, USA.
Yunhe GaoStanford University, Stanford, CA, USA.
Akshay S ChaudhariStanford University, Stanford, CA, USA.ORCID 0000-0002-3667-6796
Curtis P LanglotzStanford University, Stanford, CA, USA.ORCID 0000-0002-8972-8051
Ulas BagciNorthwestern University, Illinois, USA.ORCID 0000-0001-7379-6829
Sergio DecherchiIstituto Italiano di Tecnologia, Genoa, Italy.
Andrea CavalliIstituto Italiano di Tecnologia, Genoa, Italy.
Arkadiusz SitekHarvard Medical School, Boston, MA, USA.
Kang WangUniversity of California, San Francisco, CA, USA.
Yang YangUniversity of California, San Francisco, CA, USA.ORCID 0000-0002-2841-4243
Alan L YuilleJohns Hopkins University, Baltimore, MD, USA.
Zongwei ZhouJohns Hopkins University, Baltimore, MD, USA.

Funding

Automated Multi-Timepoint CT Analysis for Enhanced Metastatic Disease Evaluation in Colorectal Cancer: An Anatomy-Aware Vision-Language AI ApproachR01EB037669 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Kang Wang, Yang Yang · 2025 to 2026
$1.4M
NIBIB NIH HHS R01 EB037669
6 · The paper itself

Abstract

More than 300 million computed tomography (CT) scans are performed worldwide each year, yet many early or incidental tumors in these scans remain undetected. Artificial intelligence (AI) could help: segmentation models can surpass radiologists and alternative AI models in detecting tumors, and they localize the tumors for radiologist verification. However, segmentation-based tumor detection has long been limited by the need for tumor masks: radiologist-drawn tumor outlines that are scarce, expensive, and entirely unavailable for many cancer types. In contrast, nearly every CT scan is accompanied by a radiology report with detailed tumor descriptions. Yet, these reports have not been used effectively to train tumor segmentation models. Here, we introduce R-Super, a framework that converts routine radiology and pathology reports into localized training signals for tumor segmentation. R-Super trains AI to segment tumors that match their descriptions in reports. Reports are only needed for training, not inference. We trained R-Super on 127,496 CT-Report pairs (42 million 2D images, USA) and evaluated it internally at UCSF (

Identifiers

PMID42523553
PMCPMC13405427

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.