Evidence map›Paper›PMID 31846222›Full record

ArticleCancer medicine2020

A novel model to predict cancer-specific survival in patients with early-stage uterine papillary serous carcinoma (UPSC).

Lihua Chen, Xiaona Liu, Mengjiao Li, Shuoer Wang, Hongyu Zhou, Lei Liu, Xi Cheng

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 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

7 authors at 3 institutions in 1 country.

Lihua ChenDepartment of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.ORCID 0000-0002-0675-1425
Xiaona LiuShanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Mengjiao LiDepartment of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Shuoer WangCentral Laboratory, The Fifth People's Hospital of Shanghai Affiliated to Fudan University, Shanghai, China.
Hongyu ZhouDepartment of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Lei LiuShanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Xi ChengDepartment of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Shanghai Medical College of Fudan University · CNShanghai Public Health Clinical Center · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveStage I-II uterine papillary serous carcinoma (UPSC) has aggressive biological behavior and leads to poor prognosis. However, clinicopathologic risk factors to predict cancer-specific survival of patients with stage I-II UPSC were still unclear. This study was undertaken to develop a prediction model of survival in patients with early-stage UPSC.

methodsUsing Surveillance, Epidemiology, and End Results (SEER) database, 964 patients were identified with International Federation of Gynecology and Obstetrics (FIGO) stage I-II UPSC who underwent at least hysterectomy between 2004 and 2015. By considering competing risk events for survival outcomes, we used proportional subdistribution hazards regression to compare cancer-specific death (CSD) for all patients. Based on the results of univariate and multivariate analysis, the variables were selected to construct a predictive model; and the prediction results of the model were visualized using a nomogram to predict the cancer-specific survival and the response to adjuvant chemotherapy and radiotherapy of stage I-II UPSC patients.

resultsThe median age of the cohort was 67 years. One hundred and sixty five patients (17.1%) died of UPSC (CSD), while 8.6% of the patients died from other causes (non-CSD). On multivariate analysis, age ≥ 67 (HR = 1.45, P = .021), tumor size ≥ 2 cm (HR = 1.81, P = .014) and >10 regional nodes removed (HR = 0.52, P = .002) were significantly associated with cumulative incidence of CSD. In the age ≥67 cohort, FIGO stage IB-II was a risk factor for CSD (HR = 1.83, P = .036), and >10 lymph nodes removed was a protective factor (HR = 0.50, P = .01). Both adjuvant chemotherapy combined with radiotherapy and adjuvant chemotherapy alone decreased CSD of patients with stage I-II UPSC older than 67 years (HR = 0.47, P = .022; HR = 0.52, P = .024, respectively). The prediction model had great risk stratification ability as the high-risk group had higher cumulative incidence of CSD than the low-risk group (P < .001). In the high-risk group, patients with post-operative adjuvant chemoradiotherapy had improved CSD compared with patients who did not receive radiotherapy nor chemotherapy (P = .037). However, there was no such benefit in the low-risk group.

conclusionOur prediction model of CSD based on proportional subdistribution hazards regression showed a good performance in predicting the cancer-specific survival of early-stage UPSC patients and contributed to guide clinical treatment decision, helping oncologists and patients with early-stage UPSC to decide whether to choose adjuvant therapy or not.

Indexed as

HysterectomyNomogramsAgedChemoradiotherapy, AdjuvantClinical Decision-MakingCystadenocarcinoma, SerousDecision Making, SharedDecision Support TechniquesDisease-Free SurvivalFemaleFollow-Up StudiesHumansKaplan-Meier EstimateLymphatic MetastasisLymph Node ExcisionLymph Nodescancer-specific death (CSD)competing risk modelnomogramprediction modeluterine papillary serous carcinoma (UPSC)

Identifiers

PMID31846222
PMCPMC6997089
OpenAlexW2995584106

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LicenceCC BY
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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.