Evidence map›Paper›PMID 39049721›Full record

ReviewGut and liver2024

Accuracy Goals in Predicting Preoperative Lymph Node Metastasis for T1 Colorectal Cancer Resected Endoscopically.

Katsuro Ichimasa, Shin-Ei Kudo, Masashi Misawa, Khay Guan Yeoh, Tetsuo Nemoto, Yuta Kouyama, Yuki Takashina, Hideyuki Miyachi

Abstract readReview
In one paragraph

Review in Gut and liver, 2024. 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

8 authors.

Katsuro IchimasaDigestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0001-6675-1219
Shin-Ei KudoDigestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-4268-1217
Masashi MisawaDigestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-8520-2036
Khay Guan YeohYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0002-7802-4606
Tetsuo NemotoDepartment of Pathology and Laboratory Medicine, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0001-8959-2601
Yuta KouyamaDigestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-9663-247X
Yuki TakashinaDigestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-2473-7520
Hideyuki MiyachiDigestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-8404-0899

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Submucosal invasive (T1) colorectal cancer is a significant clinical management challenge, with an estimated 10% of patients developing extraintestinal lymph node metastasis. This condition necessitates surgical resection along with lymph node dissection to achieve a curative outcome. Thus, the precise preoperative assessment of lymph node metastasis risk is crucial to guide treatment decisions after endoscopic resection. Contemporary clinical guidelines strive to identify a low-risk cohort for whom endoscopic resection will suffice, applying stringent criteria to maximize patient safety. Those failing to meet these criteria are often recommended for surgical resection, with its associated mortality risks although it may still include patients with a low risk of metastasis. In the quest to enhance the precision of preoperative lymph node metastasis risk prediction, innovative models leveraging artificial intelligence or nomograms are being developed. Nevertheless, the debate over the ideal sensitivity and specificity for such models persists, with no consensus on target metrics. This review puts forth postoperative mortality rates as a practical benchmark for the sensitivity of predictive models. We underscore the importance of this method and advocate for research to amass data on surgical mortality in T1 colorectal cancer. Establishing specific benchmarks for predictive accuracy in lymph node metastasis risk assessment will hopefully optimize the treatment of T1 colorectal cancer.

Indexed as

Colorectal NeoplasmsLymphatic MetastasisHumansLymph Node ExcisionLymph NodesNeoplasm StagingNomogramsPredictive Value of TestsPreoperative PeriodRisk AssessmentColorectal neoplasmsEndoscopic mucosal resectionLymph nodesNeoplasm metastasisRisk factors

Identifiers

PMID39049721
PMCPMC11391136

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.