Evidence map›Paper›PMID 38014112›Full record

ArticleResearch square2023

The prognostic value of tumor-stroma ratio and a newly developed computer-aided quantitative analysis of routine H&E slides in high-grade serous ovarian cancer.

Lilian van Wagensveld, Cedric Walker, Kerstin Hahn, Joyce Sanders, Roy Kruitwagen, Maaike van der Aa, Gabe Sonke, Sven Rottenberg, Koen Van de Vijver, Andrew Janowczyk and 1 more

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2023. 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, 4 citations in OpenAlex.

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

11 authors at 3 institutions in 5 countries.

Lilian van WagensveldNetherlands Comprehensive Cancer Organization.
Cedric WalkerUniversity of Bern.
Kerstin HahnRoche.ORCID 0000-0003-3602-4234
Joyce SandersThe Netherlands Cancer Institute.
Roy KruitwagenMaastricht University Medical Centre.
Maaike van der AaNetherlands Comprehensive Cancer Organization (IKNL).
Gabe SonkeNKI.
Sven RottenbergThe Netherlands Cancer Institute.
Koen Van de VijverGhent University Hospital.
Andrew JanowczykEmory University and Georgia Institute of Technology.
Hugo HorlingsThe Netherlands Cancer Institute.ORCID 0000-0003-4782-8828
University of Bern · CHGhent University Hospital · BEMaastricht University Medical Centre · NL

Funding

KPMP Kidney Mapping and Atlas Project (KMAP)U01DK133090 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$10.4M
Computational Pathology of Proteinuric DiseasesR01DK118431 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI BARISONI, LAURA MARIACHIARA, HODGIN, JEFFREY BENTON · 2018 to 2025
$3.6M
Histotools: scaling digital pathology curation tools for quality control, annotation, labeling, and dataset identificationR01LM013864 · NLM · EMORY UNIVERSITY · PI JANOWCZYK, ANDREW ROBERT · 2022 to 2025
$1.4M
RadxTools for assessing tumor treatment response on imagingU01CA248226 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI TIWARI, PALLAVI, VISWANATH, SATISH EASWAR · 2020 to 2022
$1.4M
HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identificationU01CA239055 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI JANOWCZYK, ANDREW ROBERT, MADABHUSHI, ANANT · 2019 to 2022
$1.0M
NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA248226NIDDK NIH HHS R01 DK118431NIDDK NIH HHS U01 DK133090NLM NIH HHS R01 LM013864
6 · The paper itself

Abstract

Introduction: Tumor-stroma ratio (TSR) is prognostic in multiple cancers, while its role in high-grade serous ovarian cancer (HGSOC) remains unclear. Despite the prognostic insight gained from genetic profiles and tumor-infiltrating lymphocytes (TILs), the prognostic use of histology slides remains limited, while it enables the identification of tumor characteristics via computational pathology reducing scoring time and costs. To address this, this study aimed to assess TSR's prognostic role in HGSOC and its association with TILs. We additionally developed an algorithm, Ovarian-TSR (OTSR), using deep learning for TSR scoring, comparing it to manual scoring. Methods: 340 patients with advanced-stage who underwent primary debulking surgery (PDS) or neo-adjuvant chemotherapy (NACT) with interval debulking (IDS). TSR was assessed in both the most invasive (MI) and whole tumor (WT) regions through manual scoring by pathologists and quantification using OTSR. Patients were categorized as stroma-rich (≥ 50% stroma) or stroma-poor (< 50%). TILs were evaluated via immunohistochemical staining. Results: In PDS, stroma-rich tumors were significantly associated with a more frequent papillary growth pattern (60% vs 34%), while In NACT stroma-rich tumors had a lower Tumor Regression Grading (TRG 4&5, 21% vs 57%) and increased pleural metastasis (25% vs 16%). Stroma-rich patients had significantly shorter overall and progression-free survival compared to stroma-poor (31 versus 45 months; P < 0.0001, and 15 versus 17 months; P = 0.0008, respectively). Combining stromal percentage and TILs led to three distinct survival groups with good (stroma-poor, high TIL), medium (stroma-rich, high TIL, or; stroma-poor, Low TIL), and poor(stroma-rich, low TIL) survival. These survival groups remained significant in CD8 and CD103 in multivariable analysis (Hazard ratio (HR) = 1.42, 95% Confidence-interval (CI) = 1.02-1.99; HR = 1.49, 95% CI = 1.01-2.18, and HR = 1.48, 95% CI = 1.05-2.08; HR = 2.24, 95% CI = 1.55-3.23, respectively). OTSR was able to recapitulate these results and demonstrated high concordance with expert pathologists (correlation = 0.83). Conclusions: TSR is an independent prognostic factor for survival assessment in HGSOC. Stroma-rich tumors have a worse prognosis and, in the case of NACT, a higher likelihood of pleural metastasis. OTSR provides a cost and time-efficient way of determining TSR with high reproducibility and reduced inter-observer variability.

Identifiers

PMID38014112
PMCPMC10680933
OpenAlexW4388666811

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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.