Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

Bedrikovetski, Sergei; Dudi‐Venkata, Nagendra N.; Kroon, Hidde M.; Seow, Warren; Vather, Ryash; Carneiro, Gustavo; Moore, James W.; Sammour, Tarik

doi:10.1186/s12885-021-08773-w

Cited by 84 publications

(49 citation statements)

References 48 publications

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“…Simple determination of samples for modeling would lead to an overestimation for the model performance ( 96 ), and further accuracy verification for these models would be advisable. Guidelines that include external validation should be followed when reporting ML models ( 97 ). On the other hand, most of the included studies were retrospective design, leading to confounding and selection bias.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Xie

Xiong

et al. 2022

Front. Oncol.

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ObjectiveTo evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models.MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool.ResultsA total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction.ConclusionML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752

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Section: Discussionmentioning

confidence: 99%

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Xie

Xiong

et al. 2022

Front. Oncol.

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“…(82)(83)(84) It is, therefore, unsurprising that AI is also being studied in these areas. (85)(86)(87)…”

Section: Other Examples Of Research In the Use Of Ai In Colonoscopymentioning

confidence: 99%

Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications

Li¹,

Lm²

2022

smedj

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Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.

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“…A recently published metanalysis, which focused on LN staging in CRC, showed that deep learning and radiomics outperform radiologists, with deep learning also being superior to radiomics. In rectal cancer, on a per patient basis, pooled area under receiver operator characteristic curve was 0.017 for deep learning, 0.808 for radiomics, and 0.727 for radiologists; and sensitivity and specificity were 89% and 94% for deep learning, 78% and 73% for radiomics, and 68% and 70% for radiologists respectively[ 75 ].…”

Section: Clinical Stage Of Nodal Metastasesmentioning

confidence: 99%

Role of one-step nucleic acid amplification in colorectal cancer lymph node metastases detection

Crafa¹,

Vanella²,

Catalano³

et al. 2022

WJG

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Current histopathological staging procedures in colorectal cancer (CRC) depend on midline division of the lymph nodes (LNs) with one section of hematoxylin and eosin staining. Cancer cells outside this transection line may be missed, which could lead to understaging of Union for International Cancer Control Stage II high-risk patients. The one-step nucleic acid amplification (OSNA) assay has emerged as a rapid molecular diagnostic tool for LN metastases detection. It is a molecular technique that can analyze the entire LN tissue using a reverse-transcriptase loop-mediated isothermal amplification reaction to detect tumor-specific cytokeratin 19 mRNA. Our findings suggest that the OSNA assay has a high diagnostic accuracy in detecting metastatic LNs in CRC and a high negative predictive value. OSNA is a standardized, observer-independent technique, which may lead to more accurate staging. It has been suggested that in stage II CRC, the upstaging can reach 25% and these patients can access postoperative adjuvant chemotherapy. Moreover, intraoperative OSNA sentinel node evaluation may allow early CRC to be treated with organ-preserving surgery, while in more advanced-stage disease, a tailored lymphadenectomy can be performed considering the presence of aberrant lymphatic drainage and skip metastases.

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Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

Cited by 84 publications

References 48 publications

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications

Role of one-step nucleic acid amplification in colorectal cancer lymph node metastases detection

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