Evidence map›Paper›PMID 27082598›Full record

ArticleMedicine2016

Factors Predicting Effectiveness of Neoadjuvant Therapy for Esophageal Squamous Cell Carcinoma.

Yu Ohkura, Masaki Ueno, Toshiro Iizuka, Shusuke Haruta, Tsuyoshi Tanaka, Harushi Udagawa

Abstract read
In one paragraph

Article in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

6 authors.

Yu OhkuraFrom the Department of Gastroenterological Surgery (YO, MU, SH, TT, HU); and Gastroenterology (TI), Toranomon Hospital, Tokyo, Japan.
Masaki Ueno
Toshiro Iizuka
Shusuke Haruta
Tsuyoshi Tanaka
Harushi Udagawa

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of the study was to elucidate pretreatment factors that can predict the outcome of neoadjuvant chemoradiotherapy or chemotherapy (NAC(R)T) and help us choose treatment strategies appropriate for individual patients. Few studies have investigated whether clinical data obtainable before the treatment can predict the efficacy of NAC(R)T. Of 1540 patients treated for esophageal squamous cell carcinoma (ESCC) at our department between January 2000 and June 2014, those who underwent surgical resection of cStage II or more advanced ESCC after NAC(R)T (113 NACRT and 146 NACT patients) were enrolled in this study. Information all available before the treatment was analyzed to extract factors that can predict the effectiveness of NAC(R)T. NAC(R)T was considered effective when Grade 2 or greater treatment efficacy was achieved based on the histological grading system. NACRT was effective in 51 (45%) of 113 patients. The analysis of 35 pretreatment factors showed that female sex (hazard ratio [HR] = 3.650; 1.181-11.236), absence of dyslipidemia (HR = 3.284; 1.341-8.041), and histologically poorly differentiated tumor (HR = 2.431; 1.052-5.619) were factors predicting NACRT effectiveness. On the other hand, NACT was effective in 21 (14%) of 146 patients. The analysis of pretreatment factors showed that absence of dyslipidemia (HR = 10.204; 1.302-83.33) and therapy with docetaxel, cisplatin, and 5-fluorouracil (HR = 2.097; 1.027-4.280) were factors predicting NACT effectiveness. The findings of this study investigating factors that could predict the outcome of NAC(R)T suggest that the prevalence of dyslipidemia influences the outcome of NAC(R)T for ESCC.

Indexed as

AdultAgedAntineoplastic Combined Chemotherapy ProtocolsCarcinoma, Squamous CellChemoradiotherapy, AdjuvantChemotherapy, AdjuvantEsophageal NeoplasmsEsophageal Squamous Cell CarcinomaEsophagectomyFemaleHumansMaleMiddle AgedNeoplasm StagingSex Factors

Identifiers

PMID27082598
PMCPMC4839842

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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.