Evidence map›Paper›PMID 37155090›Full record

ArticleDiscover oncology2023

Necrosis score as a prognostic factor in stage I-III colorectal cancer: a retrospective multicenter study.

Huifen Ye, Yiting Wang, Su Yao, Zaiyi Liu, Changhong Liang, Yaxi Zhu, Yanfen Cui, Ke Zhao

Open access · goldAbstract read
In one paragraph

Article in Discover oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 16% of its field
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

3 citing papers in PubMed, 6 citations in OpenAlex.

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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

8 authors at 6 institutions in 1 country.

Huifen Ye *Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Yiting Wang *Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen University, 26 Yuan Cun 2 Cross Road, TianHe District, Guangzhou, 510655, China.
Su Yao *Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Zaiyi LiuDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Changhong LiangDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Yaxi ZhuDepartment of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen University, 26 Yuan Cun 2 Cross Road, TianHe District, Guangzhou, 510655, China. zhuyax@mail.sysu.edu.cn.
Yanfen CuiDepartment of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, No.3, Xinjie West Alley, Taiyuan, 030013, China. yanfen210@126.com.
Ke ZhaoDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. zhaoke@gdph.org.cn.
Guangdong Academy of Medical Sciences · CNGuangdong Provincial People's Hospital · CNKey Laboratory of Guangdong Province · CNShanxi Provincial Cancer Hospital · CNSixth Affiliated Hospital of Sun Yat-sen University · CNSun Yat-sen University · CN

Funding

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application 2022B1212010011High-level Hospital Construction Project DFJH201914High-level Hospital Construction Project DFJHBF202105National Natural Science Foundation of China 82271941Project Funded by China Postdoctoral Science Foundation 2021M700897Regional Innovation and Development Joint Fund of National Natural Science Foundation of China U22A20345the National Key R&D Program of China 2021YFF1201003the National Science Foundation for Young Scientists of China 81702322the National Science Foundation for Young Scientists of China 82202267
6 · The paper itself

Abstract

backgroundTumor necrosis results from failure to meet the requirement for rapid proliferation of tumor, related to unfavorable prognosis in colorectal cancer (CRC). However, previous studies used traditional microscopes to evaluate necrosis on slides, lacking a simultaneous phase and panoramic view for assessment. Therefore, we proposed a whole-slide images (WSIs)-based method to develop a necrosis score and validated its prognostic value in multicenter cohorts.

methodsNecrosis score was defined as the proportion of necrosis in the tumor area, semi-quantitatively classified into 3-level score groups by the cut-off of 10% and 30% on HE-stained WSIs. 768 patients from two centers were enrolled in this study, divided into a discovery (N = 445) and a validation (N = 323) cohort. The prognostic value of necrosis score was evaluated by Kaplan-Meier curves and the Cox model.

resultNecrosis score was associated with overall survival, with hazard ratio for high vs. low in discovery and validation cohorts being 2.62 (95% confidence interval 1.59-4.32) and 2.51 (1.39-4.52), respectively. The 3-year disease free survival rates of necrosis-low, middle, and high were 83.6%, 80.2%, and 59.8% in discovery cohort, and 86.5%, 84.2%, and 66.5% in validation cohort. In necrosis middle plus high subgroup, there was a trend but no significant difference in overall survival between surgery alone and adjuvant chemotherapy group in stage II CRC (P = .075).

conclusionAs a stable prognostic factor, high-level necrosis evaluated by the proposed method on WSIs was associated with unfavorable outcomes. Additionally, adjuvant chemotherapy provide survival benefits for patients with high necrosis in stage II CRC.

Indexed as

Colorectal cancerDisease free survivalNecrosis scoreOverall survivalWhole-slide images

Identifiers

PMID37155090
PMCPMC10167085
OpenAlexW4375861653

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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.