Evidence map›Paper›PMID 32194310›Full record

ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2020

A nomogram to preoperatively predict 1-year disease-specific survival in resected pancreatic cancer following neoadjuvant chemoradiation therapy.

Ho Kyoung Hwang, Keita Wada, Ha Yan Kim, Yuichi Nagakawa, Yosuke Hijikata, Yota Kawasaki, Yoshiharu Nakamura, Lip Seng Lee, Dong Sup Yoon, Woo Jung Lee and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

10 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Novel therapies targeting hypoxia mechanism to treat pancreatic cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2021
    Article
  9. Article
  10. Current epidemiology of pancreatic cancer: Challenges and opportunities.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2020
    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

11 authors at 7 institutions in 3 countries.

Ho Kyoung HwangDivision of HBP Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea.
Keita WadaDepartment of Surgery, Teikyo University School of Medicine, Tokyo 173-8605, Japan.
Ha Yan KimBiostatistician, Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Korea.
Yuichi NagakawaDepartment of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo 160-8402, Japan.
Yosuke HijikataDepartment of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo 160-8402, Japan.
Yota KawasakiDepartment of Digestive Surgery, Breast, and Thyroid Surgery, Graduate School of Medical Sciences, Kagoshima University, Kagoshima 890-0065, Japan.
Yoshiharu NakamuraDepartment of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Nippon Medical School, Tokyo 890-0065, Japan.
Lip Seng LeeDepartment of General Surgery, Changi General Hospital, Singapore 529889, Singapore.
Dong Sup YoonDivision of HBP Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea.
Woo Jung LeeDivision of HBP Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea.
Chang Moo KangDivision of HBP Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea.
Severance Hospital · KRChangi General Hospital · SGTokyo Medical University · JPKagoshima University · JPNippon Medical School · JPTeikyo University · JPYonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to develop a nomogram to predict the 1-year survival of patients with pancreatic cancer who underwent pancreatectomy following neoadjuvant treatment with preoperatively detectable clinical parameters. Extended pancreatectomy is necessary to achieve complete tumor removal in borderline resectable and locally advanced pancreatic cancer. However, it increases postoperative morbidity and mortality rates, and should be balanced with potential benefit of long-term survival.

methodsThe medical records of patients who underwent pancreatectomy following neoadjuvant treatment from January 2005 to December 2016 at Severance Hospital were retrospectively reviewed. Medical records were collected from five international institutions from Japan and Singapore for external validation.

resultsA total of 113 patients were enrolled. The nomogram for predicting 1-year disease-specific survival was created based on 5 clinically detectable preoperative parameters as follows: age (year), symptom (no/yes), tumor size at initial diagnostic stage (cm), preoperative serum carbohydrate antigen (CA) 19-9 level after neoadjuvant treatment (<34/≥34 U/mL), and planned surgery [pancreaticoduodenectomy (PD) (pylorus-preserving PD)/distal pancreatectomy (DP)/total pancreatectomy]. Model performance was assessed for discrimination and calibration. The calibration plot showed good agreement between actual and predicted survival probabilities; the the Greenwood-Nam-D'Agostino (GND) goodness-of-fit test showed that the model was well calibrated (χ

conclusionsThe developed nomogram had quite acceptable accuracy and clinical feasibility in the decision-making process for the management of pancreatic cancer.

Indexed as

neoadjuvant treatmentnomogrampancreatectomyPancreatic cancersurvival

Identifiers

PMID32194310
PMCPMC7072019
OpenAlexW3012732340

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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.