Evidence map›Paper›PMID 36358877›Full record

ArticleCancers2022

Preoperative Serum Markers and Risk Classification in Intrahepatic Cholangiocarcinoma: A Multicenter Retrospective Study.

Masaki Kaibori, Kengo Yoshii, Hisashi Kosaka, Masato Ota, Koji Komeda, Masaki Ueno, Daisuke Hokutou, Hiroya Iida, Kosuke Matsui, Mitsugu Sekimoto

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024
    Review
  5. Artificial Intelligence in Surgical Risk Prediction.Journal of clinical medicine · 2023
    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

10 authors at 6 institutions in 1 country.

Masaki KaiboriDepartment of Surgery, Kansai Medical University, Osaka 573-1191, Japan.ORCID 0000-0002-0577-3921
Kengo YoshiiDepartment of Mathematics and Statistics in Medical Sciences, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan.
Hisashi KosakaDepartment of Surgery, Kansai Medical University, Osaka 573-1191, Japan.ORCID 0000-0002-2926-9973
Masato OtaDepartment of General and Gastroenterological Surgery, Osaka Medical College, Takatsuki 569-8686, Japan.
Koji KomedaDepartment of General and Gastroenterological Surgery, Osaka Medical College, Takatsuki 569-8686, Japan.
Masaki UenoSecond Department of Surgery, Wakayama Medical University, Wakayama 641-8509, Japan.
Daisuke HokutouDepartment of Surgery, Nara Medical University, Kashihara 634-8521, Japan.ORCID 0000-0003-1661-6224
Hiroya IidaDepartment of Surgery, Shiga University of Medical Science, Otsu 520-2192, Japan.
Kosuke MatsuiDepartment of Surgery, Kansai Medical University, Osaka 573-1191, Japan.
Mitsugu SekimotoDepartment of Surgery, Kansai Medical University, Osaka 573-1191, Japan.ORCID 0000-0003-2184-538X
Kansai Medical University · JPOsaka Medical and Pharmaceutical University · JPKyoto Prefectural University of Medicine · JPNara Medical University · JPShiga University of Medical Science · JPWakayama Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate risk stratification selects patients who are expected to benefit most from surgery. This retrospective study enrolled 225 Japanese patients with intrahepatic cholangiocellular carcinoma (ICC) who underwent hepatectomy between January 2009 and December 2020 and identified preoperative blood test biomarkers to formulate a classification system that predicted prognosis. The optimal cut-off values of blood test parameters were determined by ROC curve analysis, with Cox univariate and multivariate analyses identifying prognostic factors. Risk classifications were established using classification and regression tree (CART) analysis. CART analysis revealed decision trees for recurrence-free survival (RFS) and overall survival (OS) and created three risk classifications based on machine learning of preoperative serum markers. Five-year rates differed significantly (p < 0.001) between groups: 60.4% (low-risk), 22.8% (moderate-risk), and 4.1% (high-risk) for RFS and 69.2% (low-risk), 32.3% (moderate-risk), and 9.2% (high-risk) for OS. No difference in OS was observed between patients in the low-risk group with or without postoperative adjuvant chemotherapy, although OS improved in the moderate group and was prolonged significantly in the high-risk group receiving chemotherapy. Stratification of patients with ICC who underwent hepatectomy into three risk groups for RFS and OS identified preoperative prognostic factors that predicted prognosis and were easy to understand and apply clinically.

Indexed as

classification and regression treesintrahepatic cholangiocarcinomapreoperative serum markerprognosis

Identifiers

PMID36358877
PMCPMC9658667
OpenAlexW4308579529

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.