Evidence map›Paper›PMID 39223610›Full record

ArticleBMC cancer2024

A log odds of positive lymph nodes-based predictive model effectively forecasts prognosis and guides postoperative adjuvant chemotherapy duration in stage III colon cancer: a multi-center retrospective cohort study.

Weili Zhang, Zhenlin Hou, Linjie Zhang, Xuanlin Hong, Weifeng Wang, Xiaojun Wu, Dongbo Xu, Zhenhai Lu, Jianxun Chen, Jianhong Peng

Abstract readMulticenter Study
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

10 authors.

Weili Zhang *Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
Zhenlin Hou *Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
Linjie Zhang *Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
Xuanlin HongMedical College, Shaoguan University, Shaoguan, Guangdong, 512005, People's Republic of China.
Weifeng WangDepartment of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
Xiaojun WuDepartment of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
Dongbo XuDepartment of Gastrointestinal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.
Zhenhai LuDepartment of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
Jianxun ChenDepartment of Gastrointestinal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China. lywpcjx@126.com.
Jianhong PengDepartment of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicineof Colorectal Surgery, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China. pengjh@sysucc.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe log odds of positive lymph nodes (LODDS) was considered a superior staging system to N stage in colon cancer, yet its value in determining the optimal duration of adjuvant chemotherapy for stage III colon cancer patients has not been evaluated. This study aims to assess the prognostic value of a model that combines LODDS with clinicopathological information for stage III colon cancer patients and aims to stratify these patients using the model, identifying individuals who could benefit from varying durations of adjuvant chemotherapy.

methodA total of 663 consecutive patients diagnosed with stage III colon cancer, who underwent colon tumor resection between November 2007 and June 2020 at Sun Yat-sen University Cancer Center and Longyan First Affiliated Hospital of Fujian Medical University, were enrolled in this study. Survival outcomes were analyzed using Kaplan-Meier, Cox regression. Nomograms were developed to forecast patient DFS, with the Area Under the Curve (AUC) values of time-dependent Receiver Operating Characteristic (timeROC) and calibration plots utilized to assess the accuracy and reliability of the nomograms.

resultsMultivariate analysis revealed that perineural invasion (HR = 1.776, 95% CI: 1.052-3.003, P = 0.032), poor tumor differentiation (HR = 1.638, 95% CI: 1.084-2.475, P = 0.019), and LODDS groupings of 2 and 1 (HR = 1.920, 95% CI: 1.297-2.842, P = 0.001) were independent predictors of disease-free survival (DFS) in the training cohort. Nomograms constructed from LODDS, perineural invasion, and poor tumor differentiation demonstrated robust predictive performance for 3-year and 5-year DFS in both training (3-year AUC = 0.706, 5-year AUC = 0.678) and validation cohorts (3-year AUC = 0.744, 5-year AUC = 0.762). Stratification according to this model showed that patients in the high-risk group derived significant benefit from completing 8 cycles of chemotherapy (training cohort, 82.97% vs 67.17%, P = 0.013; validation cohort, 89.49% vs 63.97%, P = 0.030).

conclusionThe prognostic model, integrating LODDS, pathological differentiation, and neural invasion, demonstrates strong predictive accuracy for stage III colon cancer prognosis. Moreover, stratification via this model offers valuable insights into optimal durations of postoperative adjuvant chemotherapy.

Indexed as

Colonic NeoplasmsNeoplasm StagingNomogramsAdultAgedChemotherapy, AdjuvantFemaleHumansLymphatic MetastasisLymph NodesMaleMiddle AgedPrognosisRetrospective StudiesColon cancerLODDSPrognosis

Identifiers

PMID39223610
PMCPMC11370012

What Socratic holds

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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 Socratic graph.