Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node

Risk factors for lymph node metastasis in T2 colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Lymph node metastasis (LNM) occurs in 20-25% of patients with T2 colorectal cancer (CRC). Identification of risk factors for LNM in T2 CRC may help identify patients who are at low risk and thereby potential candidates for endoscopic full-thickness resection. We examined risk factors for LNM in T2 CRC with the goal of establishing further criteria of the indications for endoscopic resection.

METHODS

MEDLINE, CENTRAL, and EMBASE were systematically searched from inception to November 2023. Studies that investigated the association between the presence of LNM and the clinical and pathological factors of T2 CRC were included. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Certainty of evidence (CoE) was assessed using the GRADE approach.

RESULTS

Fourteen studies (8349 patients) were included. Overall, the proportion of LNM was 22%. The meta-analysis revealed that the presence of lymphovascular invasion (OR, 5.5; 95% CI 3.7-8.3; high CoE), high-grade tumor budding (OR, 2.4; 95% CI 1.5-3.7; moderate CoE), poor differentiation (OR, 2.2; 95% CI 1.8-2.7; moderate CoE), and female sex (OR, 1.3; 95% CI 1.1-1.7; high CoE) were associated with LNM in T2 CRC. Lymphatic invasion (OR, 5.0; 95% CI 3.3-7.6) was a stronger predictor of LNM than vascular invasion (OR, 2.4; 95% CI 2.1-2.8).

CONCLUSIONS

Lymphovascular invasion, high-grade tumor budding, poor differentiation, and female sex were risk factors for LNM in T2 CRC. Endoscopic resection of T2 CRC in patients with very low risk for LNM may become an alternative to conventional surgical resection.

TRIAL REGISTRATION

PROSPERO, CRD42022316545.

Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta-analysis

OBJECTIVES

Lymphovascular invasion (LVI) is a critical risk factor for lymph node metastasis (LNM), which requires additional surgery after endoscopic resection of T1 colorectal cancer (CRC). However, the impact of additional staining on estimating LNM is unclear. This systematic review aimed to evaluate the impact of additional staining on determining LNM in T1 CRC.

METHODS

We searched five electronic databases. Outcomes were diagnostic odds ratio (DOR), assessed using hierarchical summary receiver operating characteristic curves, and interobserver agreement among pathologists for positive LVI, assessed using Kappa coefficients (κ). We performed a subgroup analysis of studies that simultaneously included a multivariable analysis for other risk factors (deep submucosal invasion, poor differentiation, and tumor budding).

RESULTS

Among the 64 studies (18,097 patients) identified, hematoxylin-eosin (HE) and additional staining for LVI had pooled sensitivities of 0.45 (95% confidence interval [CI] 0.32-0.58) and 0.68 (95% CI 0.44-0.86), specificities of 0.88 (95% CI 0.78-0.94) and 0.76 (95% CI 0.62-0.86), and DORs of 6.26 (95% CI 3.73-10.53) and 6.47 (95% CI 3.40-12.32) for determining LNM, respectively. In multivariable analysis, the DOR of additional staining for LNM (DOR 5.95; 95% CI 2.87-12.33) was higher than that of HE staining (DOR 1.89; 95% CI 1.13-3.16) (P = 0.01). Pooled κ values were 0.37 (95% CI 0.22-0.52) and 0.62 (95% CI 0.04-0.99) for HE and additional staining for LVI, respectively.

CONCLUSION

Additional staining for LVI may increase the DOR for LNM and interobserver agreement for positive LVI among pathologists.

First report from the International Evaluation of Endoscopic classification Japan NBI Expert Team: International multicenter web trial

OBJECTIVES

Narrow-band imaging (NBI) contributes to real-time optical diagnosis and classification of colorectal lesions. The Japan NBI Expert Team (JNET) was introduced in 2011. The aim of this study was to explore the diagnostic accuracy of JNET when applied by European and Japanese endoscopists not familiar with this classification.

METHODS

This study was conducted by 36 European Society of Gastrointestinal Endoscopy (ESGE) and 49 Japan Gastroenterological Endoscopy Society (JGES) non-JNET endoscopists using still images of 150 lesions. For each lesion, nonmagnified white-light, nonmagnified NBI, and magnified NBI images were presented. In the magnified NBI, the evaluation area was designated by region of interest (ROI). The endoscopists scored histological prediction for each lesion.

RESULTS

In ESGE members, the sensitivity, specificity, and accuracy were respectively 73.3%, 94.7%, and 93.0% for JNET Type 1; 53.0%, 64.9%, and 62.1% for Type 2A; 43.9%, 67.7%, and 55.1% for Type 2B; and 38.1%, 93.7%, and 85.1% for Type 3. When Type 2B and 3 were considered as one category of cancer, the sensitivity, specificity, and accuracy for differentiating high-grade dysplasia and cancer from the others were 59.9%, 72.5%, and 63.8%, respectively. These trends were the same for JGES endoscopists.

CONCLUSION

The diagnostic accuracy of the JNET classification was similar between ESGE and JGES and considered to be sufficient for JNET Type 1. On the other hand, the accuracy for Types 2 and 3 is not sufficient; however, JNET 2B lesions should be resected en bloc due to the risk of cancers and JNET 3 can be treated by surgery due to its high specificity.

Seizing the fate of lymph nodes in immunotherapy: To preserve or not?

Lymph node dissection has been a long-standing diagnostic and therapeutic strategy for metastatic cancers. However, questions over myriad related complications and survival outcomes are continuously debated. Immunotherapy, particularly neoadjuvant immunotherapy, has revolutionized the conventional paradigm of cancer treatment, yet has benefited only a fraction of patients. Emerging evidence has unveiled the role of lymph nodes as pivotal responders to immunotherapy, whose absence may contribute to drastic impairment in treatment efficacy, again posing challenges over excessive lymph node dissection. Hence, centering around this theme, we concentrate on the mechanisms of immune activation in lymph nodes and provide an overview of minimally invasive lymph node metastasis diagnosis, current best practices for activating lymph nodes, and the prognostic outcomes of omitting lymph node dissection. In particular, we discuss the potential for future comprehensive cancer treatment with effective activation of immunotherapy driven by lymph node preservation and highlight the challenges ahead to achieve this goal.

New Horizons of Artificial Intelligence in Medicine and Surgery

Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.

Long-term outcomes after endoscopic removal of malignant colorectal polyps: Results from a 10-year cohort

BACKGROUND

Choosing an optimal post-polypectomy management strategy of malignant colorectal polyps is challenging, and evidence regarding a surveillance-only strategy is limited.

AIM

To evaluate long-term outcomes after endoscopic removal of malignant colorectal polyps.

METHODS

A single-center retrospective cohort study was conducted to evaluate outcomes after endoscopic removal of malignant colorectal polyps between 2010 and 2020. Residual disease rate and nodal metastases after secondary surgery and local and distant recurrence rate for those with at least 1 year of follow-up were investigated. Event rates for categorical variables and means for continuous variables with 95% confidence intervals were calculated, and Fisher's exact test and Mann-Whitney test were performed. Potential risk factors of adverse outcomes were determined with univariate and multivariate logistic regression models.

RESULTS

In total, 135 lesions (mean size: 22.1 mm; location: 42% rectal) from 129 patients (mean age: 67.7 years; 56% male) were enrolled. The proportion of pedunculated and non-pedunculated lesions was similar, with en bloc resection in 82% and 47% of lesions, respectively. Tumor differentiation, distance from resection margins, depth of submucosal invasion, lymphovascular invasion, and budding were reported at 89.6%, 45.2%, 58.5%, 31.9%, and 25.2%, respectively. Residual tumor was found in 10 patients, and nodal metastasis was found in 4 of 41 patients who underwent secondary surgical resection. Univariate analysis identified piecemeal resection as a risk factor for residual malignancy (odds ratio: 1.74; P = 0.042). At least 1 year of follow-up was available for 117 lesions from 111 patients (mean follow-up period: 5.59 years). Overall, 54%, 30%, 30%, 11%, and 16% of patients presented at the 1-year, 3-year, 5-year, 7-year, and 9-10-year surveillance examinations. Adverse outcomes occurred in 9.0% (local recurrence and dissemination in 4 patients and 9 patients, respectively), with no difference between patients undergoing secondary surgery and surveillance only.

CONCLUSION

Reporting of histological features and adherence to surveillance colonoscopy needs improvement. Long-term adverse outcome rates might be higher than previously reported, irrespective of whether secondary surgery was performed.

Differentiation grade as a risk factor for lymph node metastasis in T1 colorectal cancer

Objectives

Japanese guidelines include high-grade (poorly differentiated) tumors as a risk factor for lymph node metastasis (LNM) in T1 colorectal cancer (CRC). However, whether the grading is based on the least or most predominant component when the lesion consists of two or more levels of differentiation varies among institutions. This study aimed to investigate which method is optimal for assessing the risk of LNM in T1 CRC.

Methods

We retrospectively evaluated 971 consecutive patients with T1 CRC who underwent initial or additional surgical resection from 2001 to 2021 at our institution. Tumor grading was divided into low-grade (well- to moderately differentiated) and high-grade based on the least or predominant differentiation analyses. We investigated the correlations between LNM and these two grading analyses.

Results

LNM was present in 9.8% of patients. High-grade tumors, as determined by least differentiation analysis, accounted for 17.0%, compared to 0.8% identified by predominant differentiation analysis. A significant association with LNM was noted for the least differentiation method (p < 0.05), while no such association was found for predominant differentiation (p = 0.18). In multivariate logistic regression, grading based on least differentiation was an independent predictor of LNM (p = 0.04, odds ratio 1.68, 95% confidence interval 1.00-2.83). Sensitivity and specificity for detecting LNM were 27.4% and 84.1% for least differentiation, and 2.1% and 99.3% for predominant differentiation, respectively.

Conclusions

Tumor grading via least differentiation analysis proved to be a more reliable measure for assessing LNM risk in T1 CRC compared to grading by predominant differentiation.

[Prognosis Analysis of Additional Surgical Treatment for High-Risk T1 Colorectal Cancer Patients After Endoscopic Resection]

Objective

To analyze the effect of additional surgery on the survival and prognosis of high-risk T1 colorectal cancer patients who have undergone endoscopic resection.

Methods

The clinical data of patients with high-risk T1 colorectal cancer were retrospectively collected. The patients were divided into the endoscopic resection (ER) plus additional surgical resection (SR) group, or the ER+SR group, and the ER group according to whether additional SR were performed after ER. Baseline data of the patients and information on the location, size, and postoperative pathology of the lesions were collected. Patient survival-related information was obtained through the medical record system and patient follow-up. The primary outcome indicators were the overall survival and the colorectal cancer-specific survival. Univariate Cox regression analysis was used to screen survival-related risk factors and hazard ratio (HR) was calculated. Multivariate Cox regression analysis was used to analyze the independent influencing factors.

Results

The data of 109 patients with T1 high-risk colorectal cancer were collected, with 52 patients in the ER group and 57 patients in the ER+SR group. The mean age of patients in the ER group was higher than that in the ER+SR group (65.21 years old vs. 60.54 years old, P=0.035), and the median endoscopic measurement of the size of lesions in the ER group was slightly lower than that in the ER+SR group (2.00 cm vs. 2.50 cm, P=0.026). The median follow-up time was 30.00 months, with the maximum follow-up time being 119 months, in the ER+SR group and there were 4 patients deaths, including one colorectal cancer-related death. Whereas the median follow-up time in the ER group was 28.50 months, with the maximum follow-up time being 78.00 months, and there were 4 patient deaths, including one caused by colorectal cancer. The overall 5-year cumulative survival rates in the ER+SR group and the ER group were 94.44% and 81.65%, respectively, and the cancer-specific 5-year cumulative survival rates in the ER+SR group and the ER group were 97.18% and 98.06%, respectively. The Kaplan-Meier analysis showed no significant difference in the overall cumulative survival or cancer-specific cumulative survival between the ER+SR and the ER groups. Univariate Cox regression analysis showed that age and the number of reviews were the risk factors of overall survival (HR=1.16 and HR=0.27, respectively), with age identified as an independent risk factor of overall survival in the multivariate Cox regression analysis (HR=1.10, P=0.045).

Conclusion

For T1 colorectal cancer patients with high risk factors after ER, factors such as patient age and their personal treatment decisions should not be overlooked. In clinical practice, additional caution should be exercised in decision-making concerning additional surgery.

Human-artificial intelligence interaction in gastrointestinal endoscopy

The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human–AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

Challenges in Implementing Endoscopic Resection for T2 Colorectal Cancer

The current standard treatment for muscularis propria-invasive (T2) colorectal cancer is surgical colectomy with lymph node dissection. With the advent of new endoscopic resection techniques, such as endoscopic full-thickness resection or endoscopic intermuscular dissection, T2 colorectal cancer, with metastasis to 20%-25% of the dissected lymph nodes, may be the next candidate for endoscopic resection following submucosal-invasive (T1) colorectal cancer. We present a novel endoscopic treatment strategy for T2 colorectal cancer and suggest further study to establish evidence on oncologic and endoscopic technical safety for its clinical implementation.

Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review

BACKGROUND

Risk evaluation of lymph node metastasis for early-stage (T1 and T2) colorectal cancers is critical for determining therapeutic strategies. Traditional methods of lymph node metastasis prediction have limited accuracy. This systematic review aimed to review the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers.

METHODS

A comprehensive search was performed of papers that evaluated the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers. Studies were appraised using the Joanna Briggs Institute tools. The primary outcome was summarizing artificial intelligence models and their accuracy. Secondary outcomes included influential variables and strategies to address challenges.

RESULTS

Of 3190 screened manuscripts, 11 were included, involving 8648 patients from 1996 to 2023. Due to diverse artificial intelligence models and varied metrics, no data synthesis was performed. Models included random forest algorithms, support vector machine, deep learning, artificial neural network, convolutional neural network and least absolute shrinkage and selection operator regression. Artificial intelligence models' area under the curve values ranged from 0.74 to 0.9993 (slide level) and 0.9476 to 0.9956 (single-node level), outperforming traditional clinical guidelines.

CONCLUSION

Artificial intelligence models show promise in predicting lymph node metastasis in early-stage colorectal cancers, potentially refining clinical decisions and improving outcomes.

PROSPERO REGISTRATION NUMBER

CRD42023409094.

Diagnostic Accuracy of Highest-Grade or Predominant Histological Differentiation of T1 Colorectal Cancer in Predicting Lymph Node Metastasis: A Systematic Review and Meta-Analysis

INTRODUCTION

Treatment guidelines for colorectal cancer (CRC) suggest 2 classifications for histological differentiation-highest grade and predominant. However, the optimal predictor of lymph node metastasis (LNM) in T1 CRC remains unknown. This systematic review aimed to evaluate the impact of the use of highest-grade or predominant differentiation on LNM determination in T1 CRC.

METHODS

The study protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO, registration number: CRD42023416971) and was published in OSF ( https://osf.io/TMAUN/ ) on April 13, 2023. We searched 5 electronic databases for studies assessing the diagnostic accuracy of highest-grade or predominant differentiation to determine LNM in T1 CRC. The outcomes were sensitivity and specificity. We simulated 100 cases with T1 CRC, with an LNM incidence of 11.2%, to calculate the differences in false positives and negatives between the highest-grade and predominant differentiations using a bootstrap method.

RESULTS

In 42 studies involving 41,290 patients, the differentiation classification had a pooled sensitivity of 0.18 (95% confidence interval [CI] 0.13-0.24) and 0.06 (95% CI 0.04-0.09) ( P < 0.0001) and specificity of 0.95 (95% CI 0.93-0.96) and 0.98 (95% CI 0.97-0.99) ( P < 0.0001) for the highest-grade and predominant differentiations, respectively. In the simulation, the differences in false positives and negatives between the highest-grade and predominant differentiations were 3.0% (range 1.6-4.4) and -1.3% (range -2.0 to -0.7), respectively.

DISCUSSION

Highest-grade differentiation may reduce the risk of misclassifying cases with LNM as negative, whereas predominant differentiation may prevent unnecessary surgeries. Further studies should examine differentiation classification using other predictive factors.

Construction of machine learning models based on transrectal ultrasound combined with contrast-enhanced ultrasound to predict preoperative regional lymph node metastasis of rectal cancer

Purpose

Constructing a machine learning model based on transrectal ultrasound (TRUS) combined with contrast-enhanced ultrasound (CEUS) to predict preoperative regional lymph node metastasis (RLNM) of rectal cancer and provide new references for decision-making.

Materials and methods

233 patients with rectal cancer were enrolled and underwent TRUS and CEUS prior to surgery. Clinicopathological and ultrasound data were collected to analyze the correlation of RLNM status, clinical features and ultrasound parameters. A 75% training set and 25% test set were utilized to construct seven machine learning algorithms. The DeLong test was used to assess the model's diagnostic performance, then chose the best one to predict RLNM of rectal cancer.

Results

The diagnostic performance was most dependent on the following: MMT difference (36), length (30), location (29), AUC ratio (27), and PI ratio (24). The prediction accuracy, sensitivity, specificity, precision, and F1 score range of KNN, Bayes, MLP, LR, SVM, RF, and LightGBM were (0.553-0.857), (0.000-0.935), (0.600-1.000), (0.557-0.952), and (0.617-0.852), respectively. The LightGBM model exhibited the optimal accuracy (0.857) and F1 score (0.852). The AUC for machine learning analytics were (0.517-0.941, 95% CI: 0.380-0.986). The LightGBM model exhibited the highest AUC (0.941, 95% CI: 0.843-0.986), though no statistic significant showed in comparison with the SVM, LR, RF, and MLP models (P > 0.05), it was significantly higher than that of the KNN and Bayes models (P < 0.05).

Conclusion

The LightGBM machine learning model based on TRUS combined with CEUS may help predict RLNM prior to surgery and provide new references for clinical treatment in rectal cancer.

Role of the artificial intelligence in the management of T1 colorectal cancer

Approximately 10% of submucosal invasive (T1) colorectal cancers demonstrate extraintestinal lymph node metastasis, necessitating surgical intervention with lymph node dissection. The ability to identify T1b (submucosal invasion depth ≥ 1000 µm) as a risk factor for lymph node metastasis via pre-treatment endoscopy is crucial in guiding treatment strategies. Accurately distinguishing T1b from T1a (submucosal invasion depth < 1000 µm) or dysplasia remains a significant challenge for artificial intelligence (AI) systems, which require high and consistent diagnostic capabilities. Moreover, as endoscopic therapies like endoscopic full-thickness resection and endoscopic intermuscular dissection evolve, and the focus on reducing unnecessary surgeries intensifies, the initial management of T1 colorectal cancers via endoscopic treatment is anticipated to increase. Consequently, the development of highly accurate and reliable AI systems is essential, not only for pre-treatment depth assessment but also for post-treatment risk stratification of lymph node metastasis. While such AI diagnostic systems are still under development, significant advancements are expected in the near future to improve decision-making in T1 colorectal cancer management.

Management of non-curative endoscopic resection of T1 colon cancer

Endoscopic resection techniques enable en-bloc resection of T1 colon cancers. A complete removal of T1 colon cancer can be considered curative when histologic examination of the specimens shows none of the high-risk factors for lymph nodes metastases. Criteria predicting lymph nodes metastases include deep submucosal invasion, poor differentiation, lymphovascular invasion, and high-grade tumor budding. In these cases, complete (R0), local endoscopic resection is considered sufficient as negligible risk of lymph nodes metastases does not outweigh morbidity and mortality associated with surgical resection. Challenges arise when endoscopic resection is incomplete (RX/R1) or high-risk histological features are present. The risk of lymph node metastasis in T1 CRC ranges from 1% to 36.4%, depending on histologic risk factors. Presence of any risk factor labels the patient "high risk," warranting oncologic surgery with mesocolic lymphadenectomy. However, even if 70%-80% of T1-CRC patients are classified as high-risk, more than 90% are without lymph node involvement after oncological surgery. Surgical overtreatment in T1 CRC is a challenge, requiring a balance between oncologic safety and minimizing morbidity/mortality. This narrative review explores the landscape of managing non-curative T1 colon cancer, focusing on the choice between advanced endoscopic resection techniques and surgical interventions. We discuss surveillance strategies and shared decision-making, emphasizing the importance of a multidisciplinary approach.

Management after non-curative endoscopic resection of T1 rectal cancer

Since the introduction of population-based screening, increasing numbers of T1 rectal cancers are detected and removed by local endoscopic resection. Patients can be cured with endoscopic resection alone, but there is a possibility of residual tumor cells remaining after the initial resection. These can be located intraluminally at the resection site or extraluminally in the form of (lymph node) metastases. To decrease the risk of residual cells progressing towards more advanced disease, additional treatment is usually needed. However, with the currently available risk stratification models, it remains challenging to determine who should and should not be further treated after non-curative endoscopic resection. In this review, the different management strategies for patients with non-curatively treated T1 rectal cancers are discussed, along with the available evidence for each strategy and relevant considerations for clinical decision making. Furthermore, we provide practical guidance on the management and surveillance following non-curative endoscopic resection of T1 rectal cancer.

Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums

PURPOSE

To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities.

METHODS

An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities.

RESULTS

Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, year of diagnosis, age, proximity to superfund sites, and primary payer. Spatio-temporal clusters highlighted geographic areas with a statistically significant high probability of late-stage diagnoses, emphasizing the need for targeted healthcare interventions.

CONCLUSIONS

This research underlines the potential of ML in enhancing the prognostic predictions in oncology, particularly in CRC. The gradient boosting model, with its robust performance, holds promise for deployment in healthcare systems to aid early detection and formulate localized cancer prevention strategies. The study's methodology demonstrates a significant step toward utilizing AI in public health to mitigate disparities and improve cancer care outcomes.

Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases

AIM

To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images.

METHODS

This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases.

RESULTS

The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for 'urgent', 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for 'semi-urgent', 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for 'routine' and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for 'observation', respectively.

CONCLUSIONS

The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.

Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?

The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.

Influence on the accuracy in ChatGPT: Differences in the amount of information per medical field

OBJECTIVES

Although ChatGPT was not developed for medical use, there is growing interest in its use in medical fields. Understanding its capabilities and precautions for its use in the medical field is an urgent matter. We hypothesized that differences in the amounts of information published in different medical fields would be proportionate to the amounts of training ChatGPT receives in those fields, and hence its accuracy in providing answers.

STUDY DESIGN

A non-clinical experimental study.

METHODS

We administered the Japanese National Medical Examination to GPT-3.5 and GPT-4 to examine the rates of accuracy and consistency in their responses. We counted the total number of documents in the Web of Science Core Collection per medical field and assessed the relationship with ChatGPT's accuracy. We also performed multivariate-adjusted models to investigate the risk factors for incorrect answers.

RESULTS

For GPT-4, we confirmed an accuracy rate of 81.0 % and a consistency rate of 88.8 % on the exam; both showed improvement compared to those for GPT-3.5. A positive correlation was observed between the accuracy rate and consistency rate (R = 0.51, P < 0.001). The number of documents per medical field was significantly correlated with the accuracy rate in that medical field (R = 0.44, P < 0.05), with relatively few publications being an independent risk factor for incorrect answers.

CONCLUSIONS

Checking consistency may help identify incorrect answers when using ChatGPT. Users should be aware that the accuracy of the answers by ChatGPT may decrease when it is asked about topics with limited published information, such as new drugs and diseases.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Lymph Node Molecular Analysis with OSNA Enables the Identification of pT1 CRC Patients at Risk of Recurrence: A Multicentre Study

Early-stage colorectal carcinoma (CRC)-pT1-is a therapeutic challenge and presents some histological features related to lymph node metastasis (LNM). A significant proportion of pT1 CRCs are treated surgically, resulting in a non-negligible surgical-associated mortality rate of 1.5-2%. Among these cases, approximately 6-16% exhibit LNM, but the impact on survival is unclear. Therefore, there is an unmet need to establish an objective and reliable lymph node (LN) staging method to optimise the therapeutic management of pT1 CRC patients and to avoid overtreating or undertreating them. In this multicentre study, 89 patients with pT1 CRC were included. All histological features associated with LNM were evaluated. LNs were assessed using two methods, One-Step Nucleic Acid Amplification (OSNA) and the conventional FFPE plus haematoxylin and eosin (H&E) staining. OSNA is an RT-PCR-based method for amplifying CK19 mRNA. Our aim was to assess the performance of OSNA and H&E in evaluating LNs to identify patients at risk of recurrence and to optimise their clinical management. We observed an 80.9% concordance in LN assessment using the two methods. In 9% of cases, LNs were found to be positive using H&E, and in 24.7% of cases, LNs were found to be positive using OSNA. The OSNA results are provided as the total tumour load (TTL), defined as the total tumour burden present in all the LNs of a surgical specimen. In CRC, a TTL ≥ 6000 CK19 m-RNA copies/µL is associated with poor prognosis. Three patients had TTL > 6000 copies/μL, which was associated with higher tumour budding. The discrepancies observed between the OSNA and H&E results were mostly attributed to tumour allocation bias. We concluded that LN assessment with OSNA enables the identification of pT1 CRC patients at some risk of recurrence and helps to optimise their clinical management.

Whole slide image-based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence

OBJECTIVES

Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for determining the need for surgery after endoscopic resection because LNM occurs in 10%. We aimed to develop a novel artificial intelligence (AI) system using whole slide images (WSIs) to predict LNM.

METHODS

We conducted a retrospective single center study. To train and test the AI model, we included LNM status-confirmed T1 and T2 CRC between April 2001 and October 2021. These lesions were divided into two cohorts: training (T1 and T2) and testing (T1). WSIs were cropped into small patches and clustered by unsupervised K-means. The percentage of patches belonging to each cluster was calculated from each WSI. Each cluster's percentage, sex, and tumor location were extracted and learned using the random forest algorithm. We calculated the areas under the receiver operating characteristic curves (AUCs) to identify the LNM and the rate of over-surgery of the AI model and the guidelines.

RESULTS

The training cohort contained 217 T1 and 268 T2 CRCs, while 100 T1 cases (LNM-positivity 15%) were the test cohort. The AUC of the AI system for the test cohort was 0.74 (95% confidence interval [CI] 0.58-0.86), and 0.52 (95% CI 0.50-0.55) using the guidelines criteria (P = 0.0028). This AI model could reduce the 21% of over-surgery compared to the guidelines.

CONCLUSION

We developed a pathologist-independent predictive model for LNM in T1 CRC using WSI for determination of the need for surgery after endoscopic resection.

TRIAL REGISTRATION

UMIN Clinical Trials Registry (UMIN000046992, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053590).

An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer

Background

According to guidelines, a lot of patients with T1 colorectal cancers (CRCs) undergo additional surgery with lymph node dissection after being treated by endoscopic resection (ER) despite the low incidence of lymph node metastasis (LNM).

Aim

The aim of this study was to develop an artificial intelligence (AI) model to more effectively identify T1 CRCs at risk for LNM and reduce the rate of unnecessary additional surgery.

Methods

We retrospectively analyzed 651 patients with T1 CRCs. The patient cohort was randomly divided into a training set (546 patients) and a test set (105 patients) (ratio 5:1), and a classification and regression tree (CART) algorithm was trained on the training set to develop a predictive AI model for LNM. The model used 12 clinicopathological factors to predict positivity or negativity for LNM. To compare the performance of the AI model with the conventional guidelines, the test set was evaluated according to the Japanese Society for Cancer of the Colon and Rectum (JSCCR) and National Comprehensive Cancer Network (NCCN) guidelines. Finally, we tested the performance of the AI model using the test set and compared it with the JSCCR and NCCN guidelines.

Results

The AI model had better predictive performance (AUC=0.960) than the JSCCR (AUC=0.588) and NCCN guidelines (AUC=0.850). The specificity (85.8% vs. 17.5%, p<0.001), balanced accuracy (92.9% vs. 58.7%, p=0.001), and the positive predictive value (36.3% vs. 9.0%, p=0.001) of the AI model were significantly better than those of the JSCCR guidelines and reduced the percentage of the high-risk group for LNM from 83.8% (JSCCR) to 20.9%. The specificity of the AI model was higher than that of the NCCN guidelines (85.8% vs. 82.4%, p=0.557), but there was no significant difference between the two. The sensitivity of the NCCN guidelines was lower than that of our AI model (87.5% vs. 100%, p=0.301), and according to the NCCN guidelines, 1.2% of the 105 test set patients had missed diagnoses.

Conclusion

The AI model has better performance than conventional guidelines for predicting LNM in T1 CRCs and therefore could significantly reduce unnecessary additional surgery.

Treatment for T1 colorectal cancers substratified by site and size: "horses for courses"

Background

Owing to advances in diagnostic technology, the diagnosis of T1 colorectal cancers (CRCs) continues to increase. However, the optimal management of T1 CRCs in the Western Hemisphere remains unclear due to limited population-based data directly comparing the efficacy of endoscopic therapy (ET) and surgical resection (SR). The purpose of this study was to report outcome data from a large Western cohort of patients who underwent ET or SR for early CRCs.

Methods

The SEER-18 database was used to identify patients with T1 CRCs diagnosed from 2004 to 2018 treated with ET or SR. Multivariable logistic regression models were employed to identify variables related to lymph node metastasis (LNM). Rates of ET and 1-year relative survival were calculated for each year. Effect of ET or SR on overall survival and cancer-specific survival was compared using Kaplan-Meier method stratified by tumor size and site.

Results

A total of 28,430 T1 CRCs patients were identified from 2004 to 2018 in US, with 22.7% undergoing ET and 77.3% undergoing SR. The incidence of T1 CRCs was 6.15 per 100,000 person-years, with male patients having a higher incidence. Left-sided colon was the most frequent location of tumors. The utilization of ET increased significantly from 2004 to 2018, with no significant change in 1-year relative survival rate. Predictors of LNM were age at diagnosis, sex, race, tumor size, histology, grade, and location. The 5-year relative survival rates were 91.4 and 95.4% for ET and SR, respectively. Subgroup analysis showed that OS and CSS were similar between ET and SR in T1N0M0 left-sided colon cancers with tumors 2 cm or less and in rectal cancers with tumors 1 cm or less.

Conclusion

Our study showed that ET was feasible and safe for patients with left-sided T1N0M0 colon cancers and tumors of 2 cm or less, as well as T1N0M0 rectal cancers and tumors of 1 cm or less. Therefore, the over- and under-use of ET should be avoided by carefully selecting patients based on tumor size and site.

Colorectal Carcinomas: Searching for New Histological Parameters Associated with Lymph Node Metastases

Background and Objectives: Colorectal cancer (CRC) continues to be an essential public health problem. Our study aimed to evaluate the prognostic significance of classic prognostic factors and some less-studied histopathological parameters in CRC. Materials and Methods: We performed a retrospective study on 71 colorectal carcinoma patients who underwent surgery at the "Pius Brînzeu" County Clinical Emergency Hospital in Timișoara, Romania. We analyzed the classic parameters but also tumor budding (TB), poorly differentiated clusters (PDCs) of cells, tumor-infiltrating lymphocytes (TILs), and the configuration of the tumor border on hematoxylin-eosin slides. Results: A high degree of malignancy (p = 0.006), deep invasion of the intestinal wall (p = 0.003), an advanced stage of the disease (p < 0.0001), lymphovascular invasion (p < 0.0001), perineural invasion (p < 0.0001), high-grade TB (p < 0.0001), high-grade PDCs (p < 0.0001), infiltrative tumor border configuration (p < 0.0001) showed a positive correlation with lymph node metastases. Conclusions: The analyzed parameters positively correlate with unfavorable prognostic factors in CRC. We highlight the value of classic prognostic factors along with a series of less-known parameters that are more accessible and easier to evaluate using standard staining techniques and that could predict the risk of relapse or aggressive evolution in patients with CRC.

CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer

OBJECTIVES

The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC).

METHODS

A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index.

RESULTS

LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01-0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06-0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89.

CONCLUSIONS

There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection.

KEY POINTS

• LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.

Tn as a potential predictor for regional lymph node metastasis in T1 colorectal cancer

BACKGROUND

Approximately 10 percent of T1 colorectal cancer (CRC) has lymph node metastasis. In this study, we aimed to determine possible predictors for nodal involvement in order to aid selection of appropriate patients for organ-preserving strategies.

METHODS

We retrospectively reviewed CRC patients underwent radical surgery from January 2009 to December 2016, with final pathology report disclosed as T1 lesion. The paraffin-embedded samples were achieved for glycosylated proteins expression analysis by immunohistochemistry.

RESULTS

Totally, 111 CRC patients with T1 lesion were enrolled in this study. Of these patients, seventeen patients had nodal metastases, with the lymph node positive rate of 15.3%. Semiquantitative analysis of immunohistochemical results indicated that mean value of Tn protein expression in T1 CRC specimens was significantly different between patients with and without lymph node metastasis (63.6 vs. 27.4; p = 0.018).

CONCLUSIONS

Our data shown that Tn expression may be applied as a molecular predictor for regional lymph node metastasis in T1 CRC. Moreover, the organ-preserving strategy could be improved by proper classification of patients. The mechanism involved in expression of Tn glycosylation protein and CRC metastasis need further investigation.

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods

PURPOSE

To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.

MATERIALS AND METHODS

The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.

RESULTS

LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.

CONCLUSION

The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

Role of artificial intelligence in Barrett’s esophagus

The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.

Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma

BACKGROUND

The high incidence of early recurrence after hepatectomy for intrahepatic cholangiocarcinoma (ICC) has a detrimental effect on overall survival (OS). Machine-learning models may improve the accuracy of outcome prediction for malignancies.

METHODS

Patients who underwent curative-intent hepatectomy for ICC were identified using an international database. Three machine-learning models were trained to predict early recurrence (< 12 months after hepatectomy) using 14 clinicopathologic characteristics. The area under the receiver operating curve (AUC) was used to assess their discrimination ability.

RESULTS

In this study, 536 patients were randomly assigned to training (n = 376, 70.1%) and testing (n = 160, 29.9%) cohorts. Overall, 270 (50.4%) patients experienced early recurrence (training: n = 150 [50.3%] vs testing: n = 81 [50.6%]), with a median tumor burden score (TBS) of 5.6 (training: 5.8 [interquartile range {IQR}, 4.1-8.1] vs testing: 5.5 [IQR, 3.7-7.9]) and metastatic/undetermined nodes (N1/NX) in the majority of the patients (training: n = 282 [75.0%] vs testing n = 118 [73.8%]). Among the three different machine-learning algorithms, random forest (RF) demonstrated the highest discrimination in the training/testing cohorts (RF [AUC, 0.904/0.779] vs support vector machine [AUC, 0.671/0.746] vs logistic regression [AUC, 0.668/0.745]). The five most influential variables in the final model were TBS, perineural invasion, microvascular invasion, CA 19-9 lower than 200 U/mL, and N1/NX disease. The RF model successfully stratified OS relative to the risk of early recurrence.

CONCLUSIONS

Machine-learning prediction of early recurrence after ICC resection may inform tailored counseling, treatment, and recommendations. An easy-to-use calculator based on the RF model was developed and made available online.

Endoscopic Treatment of T1 Colorectal Cancer

Commonly accepted criteria for curative resection of T1 colorectal cancer include R0 resection with horizontal and vertical clear margins (R0), absence of lympho-vascular or vessel infiltration (L0, V0), a low to moderate histological grading (G1/2), low tumor cell budding, and limited (<1000 µm) infiltration into the submucosa. However, submucosal infiltration depth in the absence of other high-risk features has recently been questioned as a high-risk situation for lymph-node metastasis. Consequently, endoscopic resection techniques should focus on the acquisition of qualitatively and quantitively sufficient submucosal tissue. Here, we summarize the current literature on lymph-node metastasis risk after endoscopic resection of T1 colorectal cancer. Moreover, we discuss different endoscopic resection techniques with respect to the quality of the resected specimen.

Progress in the diagnosis of lymph node metastasis in rectal cancer: a review

Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.

Dilemmas in the Clinical Management of pT1 Colorectal Cancer

Implementation of population-based colorectal cancer screening programs has led to increases in the incidence of pT1 colorectal cancer. These incipient invasive cancers have a very good prognosis and can be treated locally, but more than half of these cases are treated with surgery due to the presence of histological high-risk criteria. These high-risk criteria are suboptimal, with no consensus among clinical guidelines, heterogeneity in definitions and assessment, and poor concordance in evaluation, and recent evidence suggests that some of these criteria considered high risk might not necessarily affect individual prognosis. Current criteria classify most patients as high risk with an indication for additional surgery, but only 2-10.5% have lymph node metastasis, and the residual tumor is present in less than 20%, leading to overtreatment. Patients with pT1 colorectal cancer have excellent disease-free survival, and recent evidence indicates that the type of treatment, whether endoscopic or surgical, does not significantly impact prognosis. As a result, the protective role of surgery is questionable. Moreover, surgery is a more aggressive treatment option, with the potential for higher morbidity and mortality rates. This article presents a comprehensive review of recent evidence on the clinical management of pT1 colorectal cancer. The review analyzes the limitations of histological evaluation, the prognostic implications of histological risk status and the treatment performed, the adverse effects associated with both endoscopic and surgical treatments, and new advances in endoscopic treatment.

Clinical consequences of diagnostic variability in the histopathological evaluation of early rectal cancer

INTRODUCTION

In early rectal cancer, organ sparing treatment strategies such as local excision have gained popularity. The necessity of radical surgery is based on the histopathological evaluation of the local excision specimen. This study aimed to describe diagnostic variability between pathologists, and its impact on treatment allocation in patients with locally excised early rectal cancer.

MATERIALS AND METHODS

Patients with locally excised pT1-2 rectal cancer were included in this prospective cohort study. Both quantitative measures and histopathological risk factors (i.e. poor differentiation, deep submucosal invasion, and lymphatic- or venous invasion) were evaluated. Interobserver variability was reported by both percentages and Fleiss' Kappa- (ĸ) or intra-class correlation coefficients.

RESULTS

A total of 126 patients were included. Ninety-four percent of the original histopathological reports contained all required parameters. In 73 of the 126 (57.9%) patients, at least one discordant parameter was observed, which regarded histopathological risk factors for lymph node metastases in 36 patients (28.6%). Interobserver agreement among different variables varied between 74% and 95% or ĸ 0.530-0.962. The assessment of lymphovascular invasion showed discordances in 26% (ĸ = 0.530, 95% CI 0.375-0.684) of the cases. In fourteen (11%) patients, discordances led to a change in treatment strategy.

CONCLUSION

This study demonstrated that there is substantial interobserver variability between pathologists, especially in the assessment of lymphovascular invasion. Pathologists play a key role in treatment allocation after local excision of early rectal cancer, therefore interobserver variability needs to be reduced to decrease the number of patients that are over- or undertreated.

DeAF: A multimodal deep learning framework for disease prediction

Multimodal deep learning models have been applied for disease prediction tasks, but difficulties exist in training due to the conflict between sub-models and fusion modules. To alleviate this issue, we propose a framework for decoupling feature alignment and fusion (DeAF), which separates the multimodal model training into two stages. In the first stage, unsupervised representation learning is conducted, and the modality adaptation (MA) module is used to align the features from various modalities. In the second stage, the self-attention fusion (SAF) module combines the medical image features and clinical data using supervised learning. Moreover, we apply the DeAF framework to predict the postoperative efficacy of CRS for colorectal cancer and whether the MCI patients change to Alzheimer's disease. The DeAF framework achieves a significant improvement in comparison to the previous methods. Furthermore, extensive ablation experiments are conducted to demonstrate the rationality and effectiveness of our framework. In conclusion, our framework enhances the interaction between the local medical image features and clinical data, and derive more discriminative multimodal features for disease prediction. The framework implementation is available at https://github.com/cchencan/DeAF.

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now

Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Artificial intelligence assists surgeons' decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection

BACKGROUND

Due to the difficult evaluation of the risk of anastomotic leakage (AL) after rectal cancer resection, the decision to perform a temporary ileostomy is not easily distinguishable. The aim of the present study was to develop an artificial intelligence (AI) model for identifying the risk of AL to assist surgeons in the selective implementation of a temporary ileostomy.

MATERIALS AND METHODS

The data from 2240 patients with rectal cancer who received anterior resection were collected, and these patients were divided into one training and two test cohorts. Five AI algorithms, such as support vector machine (SVM), logistic regression (LR), Naive Bayes (NB), stochastic gradient descent (SGD) and random forest (RF) were employed to develop predictive models using clinical variables and were assessed using the two test cohorts.

RESULTS

The SVM model indicated good discernment of AL, and might have increased the implementation of temporary ileostomy in patients with AL in the training cohort (p < 0.001). Following the assessment of the two test cohorts, the SVM model could identify AL in a favorable manner, which performed with positive predictive values of 0.150 (0.091-0.234) and 0.151 (0.091-0.237), and negative predictive values of 0.977 (0.958-0.988) and 0.986 (0.969-0.994), respectively. It is important to note that the implementation of temporary ileostomy in patients without AL would have been significantly reduced (p < 0.001) and which would have been significantly increased in patients with AL (p < 0.05).

CONCLUSION

The model (https://alrisk.21cloudbox.com/) indicated good discernment of AL, which may be used to assist the surgeon's decision-making of performing temporary ileostomy.

Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria

BACKGROUND

Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis.

OBJECTIVE

To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model.

METHODS

First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis.

RESULTS

The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG - 004 was the most important variable in the SVM model.

CONCLUSION

CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis.

A nomogram prediction model for lymph node metastasis risk after neoadjuvant chemoradiotherapy in rectal cancer patients based on SEER database

Background

Rectal cancer patients who received neoadjuvant chemoradiotherapy (CRT) may have a lower cancer stage and a better prognosis. Some patients may be able to avoid invasive surgery. It is critical to accurately assess lymph node metastases (LNM) after neoadjuvant chemoradiotherapy. The goal of this study is to identify clinical variables associated with LNM and to develop a nomogram for LNM prediction in rectal cancer patients following nCRT.

Methods

From 2010 to 2015, patients were drawn from the Surveillance, Epidemiology, and End Results (SEER) database. To identify clinical factors associated with LNM, the least absolute shrinkage and selection operator (LASSO) aggression and multivariate logistic regression analyses were used. To predict the likelihood of LNM, a nomogram based on multivariate logistic regression was created using decision curve analyses.

Reslut

The total number of patients included in this study was 6,388. The proportion of patients with pCR was 17.50% (n=1118), and the proportion of patients with primary tumor pCR was 20.84% (n = 1,331). The primary tumor was pCR in 16.00% (n=213) of the patients. Age, clinical T stage, clinical N stage, and histology were found to be significant independent clinical predictors of LNM using LASSO and multivariate logistic regression analysis. The nomogram was developed based on four clinical factors. The 5-year overall survival rate was 78.9 percent for those with ypN- and 66.3 percent for those with ypN+, respectively (P<0.001).

Conclusion

Patients over 60 years old, with clinical T1-2, clinical N0, and adenocarcinoma may be more likely to achieve ypN0. The watch-and-wait (WW) strategy may be considered. Patients who had ypN0 or pCR had a better prognosis.

Serum insulin-like growth factor binding protein-3 as a potential biomarker for diagnosis and prognosis of oesophageal squamous cell carcinoma

BACKGROUND

Insulin-like growth factor binding protein-3 (IGFBP3) has been reported to be related to the risk of some cancers. Here we focussed on serum IGFBP3 as a possible biomarker of diagnosis and prognosis for oesophageal squamous carcinoma (ESCC).

METHODS

Enzyme-linked immunosorbent assay (ELISA) was used to measure the serum IGFBP3 level in the training cohort including 136 ESCC patients and 119 normal controls and the validation cohort with 55 ESCC patients and 42 normal controls. The receiver operating characteristics curve (ROC) was used to assess the diagnosis value. Cox proportional hazards model was applied to select factors for survival nomogram construction.

RESULTS

Serum IGFBP3 levels were significantly lower in early-stage ESCC or ESCC patients than those in normal controls (p < .05). The specificity and sensitivity of serum IGFBP3 for the diagnosis of ESCC were 95.80% and 50.00%, respectively, with the area under the ROC curve (AUC) of 0.788 in the training cohort. Similar results were observed in the validation cohort (88.10%, 38.18%, and 0.710). Importantly, serum IGFBP3 could also differentiate early-stage ESCC from controls (95.80%, 52.54%, 0.777 and 88.10%, 36.36%, 0.695 in training and validation cohorts, respectively). Furthermore, Cox multivariate analysis revealed that serum IGFBP3 was an independent prognostic risk factor (HR = 2.599, p = .002). Lower serum IGFBP3 level was correlated with reduced overall survival (p < .05). Nomogram based on serum IGFBP3, TNM stage, and tumour size improved the prognostic prediction of ESCC with a concordance index of 0.715.

CONCLUSION

We demonstrated that serum IGFBP3 was a potential biomarker of diagnosis and prognosis for ESCC. Meanwhile, the nomogram might help predict the prognosis of ESCC. Key MessageSerum IGFBP3 showed early diagnostic value in oesophageal squamous cell carcinoma with independent cohort validation. Moreover, serum IGFBP3 was identified as an independent prognostic risk factor, which was used to construct a nomogram with improved prognosis ability in oesophageal squamous cell carcinoma.

Molecular and clinicopathological differences between depressed and protruded T2 colorectal cancer

Background

Colorectal cancer (CRC) can be classified into four consensus molecular subtypes (CMS) according to genomic aberrations and gene expression profiles. CMS is expected to be useful in predicting prognosis and selecting chemotherapy regimens. However, there are still no reports on the relationship between the morphology and CMS.

Methods

This retrospective study included 55 subjects with T2 CRC undergoing surgical resection, of whom 30 had the depressed type and 25 the protruded type. In the classification of the CMS, we first defined cases with deficient mismatch repair as CMS1. And then, CMS2/3 and CMS4 were classified using an online classifier developed by Trinh et al. The staining intensity of CDX2, HTR2B, FRMD6, ZEB1, and KER and the percentage contents of CDX2, FRMD6, and KER are input into the classifier to obtain automatic output classifying the specimen as CMS2/3 or CMS4.

Results

According to the results yielded by the online classifier, of the 30 depressed-type cases, 15 (50%) were classified as CMS2/3 and 15 (50%) as CMS4. Of the 25 protruded-type cases, 3 (12%) were classified as CMS1 and 22 (88%) as CMS2/3. All of the T2 CRCs classified as CMS4 were depressed CRCs. More malignant pathological findings such as lymphatic invasion were associated with the depressed rather than protruded T2 CRC cases.

Conclusions

Depressed-type T2 CRC had a significant association with CMS4, showing more malignant pathological findings such as lymphatic invasion than the protruded-type, which could explain the reported association between CMS4 CRC and poor prognosis.

Novel "resect and analysis" approach for T2 colorectal cancer with use of artificial intelligence

BACKGROUND AND AIMS

Because of a lack of reliable preoperative prediction of lymph node involvement in early-stage T2 colorectal cancer (CRC), surgical resection is the current standard treatment. This leads to overtreatment because only 25% of T2 CRC patients turn out to have lymph node metastasis (LNM). We assessed a novel artificial intelligence (AI) system to predict LNM in T2 CRC to ascertain patients who can be safely treated with less-invasive endoscopic resection such as endoscopic full-thickness resection and do not need surgery.

METHODS

We included 511 consecutive patients who had surgical resection with T2 CRC from 2001 to 2016; 411 patients (2001-2014) were used as a training set for the random forest-based AI prediction tool, and 100 patients (2014-2016) were used to validate the AI tool performance. The AI algorithm included 8 clinicopathologic variables (patient age and sex, tumor size and location, lymphatic invasion, vascular invasion, histologic differentiation, and serum carcinoembryonic antigen level) and predicted the likelihood of LNM by receiver-operating characteristics using area under the curve (AUC) estimates.

RESULTS

Rates of LNM in the training and validation datasets were 26% (106/411) and 28% (28/100), respectively. The AUC of the AI algorithm for the validation cohort was .93. With 96% sensitivity (95% confidence interval, 90%-99%), specificity was 88% (95% confidence interval, 80%-94%). In this case, 64% of patients could avoid surgery, whereas 1.6% of patients with LNM would lose a chance to receive surgery.

CONCLUSIONS

Our proposed AI prediction model has a potential to reduce unnecessary surgery for patients with T2 CRC with very little risk. (Clinical trial registration number: UMIN 000038257.).