Evidence map›Paper›PMID 35908272›Full record

ArticleInternational journal of clinical oncology2022

Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer.

Kenta Kasahara, Kenji Katsumata, Akira Saito, Tetsuo Ishizaki, Masanobu Enomoto, Junichi Mazaki, Tomoya Tago, Yuichi Nagakawa, Jun Matsubayashi, Toshitaka Nagao and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of clinical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

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

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
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

13 authors.

Kenta KasaharaDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan. kasadog@tokyo-med.ac.jp.ORCID http://orcid.org/0000-0001-5997-5919
Kenji KatsumataDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Akira SaitoDepartment of AI Applied Quantitative Clinical Science, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Tetsuo IshizakiDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Masanobu EnomotoDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Junichi MazakiDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Tomoya TagoDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Yuichi NagakawaDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Jun MatsubayashiDepartment of Anatomic Pathology, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Toshitaka NagaoDepartment of Anatomic Pathology, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Hiroshi HiranoDiagnostic Pathology Division, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji-shi, 193-0998, Tokyo, Japan.
Masahiko KurodaDepartment of Molecular Pathology, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Akihiko TsuchidaDepartment of Digestive and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi Shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe treatment strategies for colorectal cancer (CRC) must ensure a radical cure of cancer and prevent over/under treatment. Biopsy specimens used for the definitive diagnosis of T1 CRC were analyzed using artificial intelligence (AI) to construct a risk index for lymph node metastasis.

methodsA total of 146 T1 CRC cases were analyzed. The specimens for analysis were mainly biopsy specimens, and in the absence of biopsy specimens, the mucosal layer of the surgical specimens was analyzed. The pathology slides for each case were digitally imaged, and the morphological features of cancer cell nuclei were extracted from the tissue images. First, statistical methods were used to analyze how well these features could predict lymph node metastasis risk. A lymph node metastasis risk model using AI was created based on these morphological features, and accuracy in test cases was verified.

resultsEach developed model could predict lymph node metastasis risk with a > 90% accuracy in each region of interest of the training cases. Lymph node metastasis risk was predicted with 81.8-86.3% accuracy for randomly validated cases, using a learning model with biopsy data. Moreover, no case with lymph node metastasis or lymph node risk was judged to have no risk using the same model.

conclusionsAI models suggest an association between biopsy specimens and lymph node metastases in T1 CRC and may contribute to increased accuracy of preoperative diagnosis.

Indexed as

Artificial IntelligenceColorectal NeoplasmsBiopsyHumansLymphatic MetastasisLymph NodesArtificial intelligenceColorectal cancerLymph node metastasisSubmucosal invasion

Identifiers

PMID35908272

What OpenQuestion holds

Textmetadata
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.