Relative Rates of Missed Diagnosis for Colonoscopy, Barium Enema, and Flexible Sigmoidoscopy in 379 Patients with Colorectal Cancer

Frenette, Catherine; Strum, Williamson B.

doi:10.1007/s12029-008-9027-x

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…Current recommendations for CRC screening emphasize optical endoscopy (i.e. sigmoidoscopy or colonoscopy) [ 2 ] or fecal immunochemical test for blood (FIT) [ 3 ]. However, there are several problems encountered when endoscopy is used as a first-line screening tool.…”

Section: Introductionmentioning

confidence: 99%

Cell-surface markers for colon adenoma and adenocarcinoma

et al. 2016

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Early detection of colorectal cancer (CRC) is crucial for effective treatment. Among CRC screening techniques, optical colonoscopy is widely considered the gold standard. However, it is a costly and invasive procedure with a low rate of compliance. Our long-term goal is to develop molecular imaging agents for the non-invasive detection of CRC by molecular imaging-based colonoscopy using CT, MRI or fluorescence. To achieve this, cell surface targets must be identified and validated. Here, we report the discovery of cell-surface markers that distinguish CRC from surrounding tissues that could be used as molecular imaging targets. Profiling of mRNA expression microarray data from patient tissues including adenoma, adenocarcinoma, and normal gastrointestinal tissues was used to identify potential CRC specific cell-surface markers. Of the identified markers, six were selected for further validation (CLDN1, GPR56, GRM8, LY6G6D/F, SLCO1B3 and TLR4). Protein expression was confirmed by immunohistochemistry of patient tissues. Except for SLCO1B3, diffuse and low expression was observed for each marker in normal colon tissues. The three markers with the greatest protein overexpression were CLDN1, LY6G6D/F and TLR4, where at least one of these markers was overexpressed in 97% of the CRC samples. GPR56, LY6G6D/F and SLCO1B3 protein expression was significantly correlated with the proximal tumor location and with expression of mismatch repair genes. Marker expression was further validated in CRC cell lines. Hence, three cell-surface markers were discovered that distinguish CRC from surrounding normal tissues. These markers can be used to develop imaging or therapeutic agents targeted to the luminal surface of CRC.

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Section: Introductionmentioning

confidence: 99%

Cell-surface markers for colon adenoma and adenocarcinoma

et al. 2016

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“…A similar study by Somasekar et al ,16 found that 56 (10%) patients with a diagnosis of CRC had undergone barium enema (5%) or colonoscopy (5%) in the preceding 5 years. Another study by Frenette and Strum17 reported a failure of diagnosis in 60 (16%) patients who had undergone colonoscopy, barium enema or flexible sigmoidoscopy in the preceding 3 years. Our results are significantly lower than the cohorts of Somasekar and Frenette (p<0.005, χ 2 test), and suggest a low ‘miss-rate’ for CRC with these investigations within our unit.…”

Section: Discussionmentioning

confidence: 99%

Systematic analysis of missed colorectal cancer cases and common pitfalls in diagnosis

Vaughan-Shaw

Aung

Knight

et al. 2015

Frontline Gastroenterol

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“…Ensemble CNN model with explainable AI for the classification of gastrointestinal cancer year timeline [8]. Nonetheless, a study conducted by [9] put forward that cognitive and technological issues contribute to significant diagnostic errors, despite the effectiveness of traditional screening procedures.…”

Section: Plos Onementioning

confidence: 99%

“…However, if gastrointestinal cancers are detected in their early stages, the survival rate becomes higher in the five year timeline [ 8 ]. Nonetheless, a study conducted by [ 9 ] put forward that cognitive and technological issues contribute to significant diagnostic errors, despite the effectiveness of traditional screening procedures.…”

Section: Introductionmentioning

confidence: 99%

Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer

Auzine,

Heenaye-Mamode Khan,

Baichoo

et al. 2024

PLoS ONE

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The implementation of AI assisted cancer detection systems in clinical environments has faced numerous hurdles, mainly because of the restricted explainability of their elemental mechanisms, even though such detection systems have proven to be highly effective. Medical practitioners are skeptical about adopting AI assisted diagnoses as due to the latter’s inability to be transparent about decision making processes. In this respect, explainable artificial intelligence (XAI) has emerged to provide explanations for model predictions, thereby overcoming the computational black box problem associated with AI systems. In this particular research, the focal point has been the exploration of the Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) approaches which enable model prediction explanations. This study used an ensemble model consisting of three convolutional neural networks(CNN): InceptionV3, InceptionResNetV2 and VGG16, which was based on averaging techniques and by combining their respective predictions. These models were trained on the Kvasir dataset, which consists of pathological findings related to gastrointestinal cancer. An accuracy of 96.89% and F1-scores of 96.877% were attained by our ensemble model. Following the training of the ensemble model, we employed SHAP and LIME to analyze images from the three classes, aiming to provide explanations regarding the deterministic features influencing the model’s predictions. The results obtained from this analysis demonstrated a positive and encouraging advancement in the exploration of XAI approaches, specifically in the context of gastrointestinal cancer detection within the healthcare domain.

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Relative Rates of Missed Diagnosis for Colonoscopy, Barium Enema, and Flexible Sigmoidoscopy in 379 Patients with Colorectal Cancer

Abstract: Failure rates for colonoscopy, barium enema, and flexible sigmoidoscopy were 9%, 20%, and 50%.

Cited by 8 publications

References 40 publications

Cell-surface markers for colon adenoma and adenocarcinoma

Cell-surface markers for colon adenoma and adenocarcinoma

Systematic analysis of missed colorectal cancer cases and common pitfalls in diagnosis

Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer

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