Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.

The clinical application of artificial intelligence in cancer precision treatment

BACKGROUND

Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking.

OBSERVATIONS

By collecting and integrating large volumes of data and applying it to clinical tasks across various algorithms and models, artificial intelligence plays a significant role in cancer precision medicine. Here, we describe the general principles of artificial intelligence, including machine learning and deep learning. We further summarize the latest developments in artificial intelligence applications in cancer precision medicine. In tumor precision treatment, artificial intelligence plays a crucial role in individualizing both conventional and emerging therapies. In specific fields, including target prediction, targeted drug generation, immunotherapy response prediction, neoantigen prediction, and identification of long non-coding RNA, artificial intelligence offers promising perspectives. Finally, we outline the current challenges and ethical issues in the field.

CONCLUSIONS

Recent clinical studies demonstrate that artificial intelligence is involved in cancer precision medicine and has the potential to benefit cancer healthcare, particularly by optimizing conventional therapies, emerging targeted therapies, and individual immunotherapies. This review aims to provide valuable resources to clinicians and researchers and encourage further investigation in this field.

Preoperative CT lymph node size as a predictor of nodal metastasis in resectable Colon cancer: a retrospective study of 694 patients

PURPOSE

This study aimed to investigate the efficacy of measuring lymph node size on preoperative CT imaging to predict pathological lymph node metastasis in patients with colon cancer to enhance diagnostic accuracy and improve treatment planning by establishing more reliable assessment methods for lymph node metastasis.

METHODS

We retrospectively analyzed 1,056 patients who underwent colorectal resection at our institution between January 2004 and March 2020. From this cohort, 694 patients with resectable colon cancer were included in the study. We analyzed the relationship between lymph node size on preoperative CT imaging and lymph node metastasis identified on postoperative pathological examination.

RESULTS

The optimal cutoff values for the maximum long diameter and short diameter of regional lymph nodes on preoperative CT were identified as 6.5 mm and 5.5 mm, respectively, with an AUC of 0.7794 and 0.7755, respectively. Notably, the predictive accuracy varied by tumor location. Higher cutoff values were observed in the right-sided colon (maximum long diameter: 7.7 mm, maximum short diameter: 5.9 mm) compared to the left-sided colon (maximum long diameter: 5.8 mm, maximum short diameter: 5.2 mm).

CONCLUSION

Lymph node size on preoperative CT is a significant predictor of pathological lymph node metastasis in colon cancer. Notably, the optimal cutoff values for predicting lymph node metastasis vary depending on the specific region within the colon.

Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology

BACKGROUND AND AIMS

The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently.

METHODS

A modified Delphi process was used to develop these consensus statements.

RESULTS

Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology.

CONCLUSIONS

The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.

Efficacy of a whole slide image-based prediction model for lymph node metastasis in T1 colorectal cancer: A systematic review

BACKGROUND AND AIM

Accurate stratification of the risk of lymph node metastasis (LNM) following endoscopic resection of submucosal invasive (T1) colorectal cancer (CRC) is imperative for determining the necessity for additional surgery. In this systematic review, we evaluated the efficacy of prediction of LNM by artificial intelligence (AI) models utilizing whole slide image (WSI) in patients with T1 CRC.

METHODS

In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review was conducted through searches in PubMed (MEDLINE), Embase, and the Cochrane Library for relevant studies published up to December 2023. The inclusion criteria were studies assessing the accuracy of hematoxylin and eosin-stained WSI-based AI models for predicting LNM in patients with T1 CRC.

RESULTS

Four studies met the criteria for inclusion in this systematic review. The area under the receiver operating characteristic curve for these AI models ranged from 0.57 to 0.76. In the three studies in which AI performance was compared directly with current treatment guidelines, AI consistently exhibited a higher area under the receiver operating characteristic curve. At a fixed sensitivity of 100%, specificities ranged from 18.4% to 45.0%.

CONCLUSIONS

Artificial intelligence models based on WSI can potentially address the issue of diagnostic variability between pathologists and exceed the predictive accuracy of current guidelines. However, these findings require confirmation by larger studies that incorporate external validation.

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

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

Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review

We systematically reviewed the application of artificial intelligence (AI) in predicting lymph node metastasis (LNM) in T1 colorectal cancer (CRC). Thirteen studies with 8417 patients were included. AI demonstrated high potential in predicting LNM with sensitivity, specificity, and AUC ranging from 0.561 to 1.0, 0.45 to 1.0, and 0.717 to 1.0, respectively, reducing unnecessary surgeries by approximately 70%.

Lesion Localization and Pathological Diagnosis of Ovine Pulmonary Adenocarcinoma Based on MASK R-CNN

Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumour caused by the Jaagsiekte Sheep Retrovirus (JSRV). Histopathological diagnosis is the gold standard for OPA diagnosis. However, interpretation of traditional pathology images is complex and operator dependent. The mask regional convolutional neural network (Mask R-CNN) has emerged as a valuable tool in pathological diagnosis. This study utilized 54 typical OPA whole slide images (WSI) to extract 7167 typical lesion images containing OPA to construct a Common Objects in Context (COCO) dataset for OPA pathological images. The dataset was categorized into training and test sets (8:2 ratio) for model training and validation. Mean average specificity (mASp) and average sensitivity (ASe) were used to evaluate model performance. Six WSI-level pathological images (three OPA and three non-OPA images), not included in the dataset, were used for anti-peeking model validation. A random selection of 500 images, not included in the dataset establishment, was used to compare the performance of the model with assessment by pathologists. Accuracy, sensitivity, specificity, and concordance rate were evaluated. The model achieved a mASp of 0.573 and an ASe of 0.745, demonstrating effective lesion detection and alignment with expert annotation. In Anti-Peeking verification, the model showed good performance in locating OPA lesions and distinguished OPA from non-OPA pathological images. In the random 500-image diagnosis, the model achieved 92.8% accuracy, 100% sensitivity, and 88% specificity. The agreement rates between junior and senior pathologists were 100% and 96.5%, respectively. In conclusion, the Mask R-CNN-based OPA diagnostic model developed for OPA facilitates rapid and accurate diagnosis in practical applications.

Management of T1 Colorectal Cancer

BACKGROUND

Approximately 10% of patients with submucosal invasive (T1) colorectal cancer (CRC) have lymph node metastasis (LNM). The risk of LNM can be stratified according to various histopathological factors, such as invasion depth, lymphovascular invasion, histological grade, and tumor budding.

SUMMARY

T1 CRC with a low risk of LNM can be cured by local excision via endoscopic resection (ER), whereas surgical resection (SR) with lymph node dissection is required for high-risk T1 CRC. Current guidelines raise concern that many patients receive unnecessary SR, even though most patients achieve a radical cure. Novel diagnostic techniques for LNM, such as nomograms, artificial intelligence systems, and genomic analysis, have been recently developed to identify more low-risk T1 CRC cases. Assessing the curability and the necessity of additional treatment, including SR with lymph node dissection and chemoradiotherapy, according to histopathological findings of the specimens resected using ER, is becoming an acceptable strategy for T1 CRC, particularly for rectal cancer. Therefore, complete resection with negative vertical and horizontal margins is necessary for this strategy. Advanced ER methods for resecting the muscle layer or full thickness, which may guarantee complete resection with negative vertical margins, have been developed.

KEY MESSAGE

Although a necessary SR should not be delayed for T1 CRC given its unfavorable prognosis when SR with lymph node dissection is performed, the optimal treatment method should be chosen based on the risk of LNM and the patient's life expectancy, physical condition, social characteristics, and wishes.

Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions

With the improvement of national health awareness and the popularization of a series of screening methods, the number of patients with early colorectal cancer is gradually increasing, and accurate prediction of lymph node metastasis of T1 colorectal cancer is the key to determining the optimal therapeutic solutions. Whether patients with T1 colorectal cancer undergoing endoscopic resection require additional surgery and regional lymph node dissection is inconclusive in current guidelines. However, we can be sure that in early colorectal cancer without lymph node metastasis, endoscopic resection alone does not affect the prognosis, and it greatly improves the quality of life and reduces the incidence of surgical complications while preserving organ integrity. Therefore, it is vital to discriminate patients without lymph node metastasis in T1 colorectal cancer, and this requires accurate predictors. This paper briefly explains the significance and shortcomings of traditional pathological factors, then extends and states the new pathological factors, clinical test factors, molecular biomarkers, and the risk assessment models of lymph node metastasis based on artificial intelligence.

Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer

BACKGROUND

The low positive predictive value for lymph node metastases (LNM) of common practice risk criteria (CPRC) in T1 colorectal carcinoma (CRC) leads to manyunnecessary additional surgeries following local resection. This study aimed to identify criteria that may improve on the CPRC.

METHODS

Logistic regression analysis was performed to determine the association of diverse variables with LNM or 'poor outcome' (LNM and/or distant metastases and/or recurrence) in a single center T1 CRC cohort. The diagnostic capacity of the set of variables obtained was compared with that of the CPRC.

RESULTS

The study comprised 161 cases. Poorly differentiated clusters (PDC) and tumor budding grade > 1 (TB > 1) were the only independent variables associated with LNM. The area under the curve (AUC) for these criteria was 0.808 (CI 95% 0.717-0.880) compared to 0.582 (CI 95% 0.479-0.680) for CPRC. TB > 1 and lymphovascular invasion (LVI) were independently associated with 'poor outcome', with an AUC of 0.801 (CI 95% 0.731-0.859), while the AUC for CPRC was 0.691 (CI 95% 0.603-0.752). TB > 1, combined either with PDC or LVI, would reduce false positives between 41.5% and 45% without significantly increasing false negatives.

CONCLUSIONS

Indicating additional surgery in T1 CRC only when either TB > 1, PDC, or LVI are present could reduce unnecessary surgeries significantly.

Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens

According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1-25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758-0.830 in the training set and 0.781-0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.

Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology

INTRODUCTION

Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent.

METHODS

Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers.

RESULTS

It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925.

CONCLUSION

Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.

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.

A new clinical model for predicting lymph node metastasis in T1 colorectal cancer

PURPOSE

Lymph node metastasis (LNM) is a crucial factor that determines the prognosis of T1 colorectal cancer (CRC) patients. We aimed to develop a practical prediction model for LNM in T1 CRC.

METHODS

We conducted a retrospective analysis of data from 825 patients with T1 CRC who underwent radical resection at a single center in China. All enrolled patients were randomly divided into a training set and a validation set at a ratio of 7:3 using R software. Risk factors for LNM were identified through multivariate logistic regression analyses. Subsequently, a prediction model was developed using the selected variables.

RESULTS

The lymph node metastasis (LNM) rate was 10.1% in the training cohort and 9.3% in the validation cohort. In the training set, risk factors for LNM in T1 CRC were identified, including depressed endoscopic gross appearance, sex, submucosal invasion combined with tumor grade (DSI-TG), lymphovascular invasion (LVI), and tumor budding. LVI emerged as the most potent predictor for LNM. The prediction model based on these factors exhibited good discrimination ability in the validation sets (AUC: 79.3%). Compared to current guidelines, the model could potentially reduce over-surgery by 48.9%. Interestingly, we observed that sex had a differential impact on LNM between early-onset and late-onset CRC patients.

CONCLUSIONS

We developed a clinical prediction model for LNM in T1 CRC using five factors that are easily accessible in clinical practice. The model has better predictive performance and practicality than the current guidelines and can assist clinicians in making treatment decisions for T1 CRC patients.

[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.

Neighboring macrophage-induced alteration in the phenotype of colorectal cancer cells in the tumor budding area

BACKGROUND

A higher number of tumor buds in the invasive front of colorectal cancer (CRC) specimens has been shown to contribute to a poor prognosis in CRC patients. Because macrophages (Mφs) have been demonstrated to alter the phenotype of cancer cells, we hypothesized that the phenotype of CRC cells in the tumor budding (TB) area might be changed by the interaction between CRC cells and Mφs.

METHODS

We assessed the expression of topoisomerase 1 in CRC cells to estimate the acquisition of chemoresistance in CRC. To demonstrate the tumor-stromal interaction between CRC cells and Mφs, we assessed two histological findings, the number of Mφs per single CRC cell and the proximity between CRC cells and Mφs by histological spatial analysis using HALO software.

RESULTS

The expression levels of topoisomerase 1 in CRC cells were decreased in deeper areas, especially in the TB area, compared to the surface area. Our histological spatial analysis revealed that 2.6 Mφs located within 60 μm of a single CRC cell were required to alter the phenotype of the CRC cell. Double-immunofluorescence staining revealed that higher Mφs were positive for interleukin-6 (IL-6) in the TB area and that AE1/AE3-positive CRC cells were also positive for phospho-STAT3 (pSTAT3) in the TB area; thus, the IL-6 receptor (IL-6R)/STAT3 signaling pathway in CRC cells was upregulated by IL-6 derived from neighboring Mφs.

CONCLUSION

IL-6 secreted from the neighboring Mφs would alter the phenotype of CRC cells via IL-6R/STAT3 signaling pathway.

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.

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.

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.

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.

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.

Predicting Lymph Node Metastasis Status from Primary Muscle-Invasive Bladder Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study

BACKGROUND

Accurate prediction of lymph node metastasis (LNM) status in patients with muscle-invasive bladder cancer (MIBC) before radical cystectomy can guide the use of neoadjuvant chemotherapy and the extent of pelvic lymph node dissection. We aimed to develop and validate a weakly-supervised deep learning model to predict LNM status from digitized histopathological slides in MIBC.

METHODS

We trained a multiple instance learning model with an attention mechanism (namely SBLNP) from a cohort of 323 patients in the TCGA cohort. In parallel, we collected corresponding clinical information to construct a logistic regression model. Subsequently, the score predicted by the SBLNP was incorporated into the logistic regression model. In total, 417 WSIs from 139 patients in the RHWU cohort and 230 WSIs from 78 patients in the PHHC cohort were used as independent external validation sets.

RESULTS

In the TCGA cohort, the SBLNP achieved an AUROC of 0.811 (95% confidence interval [CI], 0.771-0.855), the clinical classifier achieved an AUROC of 0.697 (95% CI, 0.661-0.728) and the combined classifier yielded an improvement to 0.864 (95% CI, 0.827-0.906). Encouragingly, the SBLNP still maintained high performance in the RHWU cohort and PHHC cohort, with an AUROC of 0.762 (95% CI, 0.725-0.801) and 0.746 (95% CI, 0.687-0.799), respectively. Moreover, the interpretability of SBLNP identified stroma with lymphocytic inflammation as a key feature of predicting LNM presence.

CONCLUSIONS

Our proposed weakly-supervised deep learning model can predict the LNM status of MIBC patients from routine WSIs, demonstrating decent generalization performance and holding promise for clinical implementation.

Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis-A Pilot Comparative Study

We aimed to comparatively assess the prognostic preoperative value of the main peripheral blood components and their ratios-the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)-to the use of artificial-neural-network analysis in determining undesired postoperative outcomes in colorectal cancer patients. Our retrospective study included 281 patients undergoing elective radical surgery for colorectal cancer in the last seven years. The preoperative values of SII, NLR, LMR, and PLR were analyzed in relation to postoperative complications, with a special emphasis on their ability to accurately predict the occurrence of anastomotic leak. A feed-forward fully connected multilayer perceptron network (MLP) was trained and tested alongside conventional statistical tools to assess the predictive value of the abovementioned blood markers in terms of sensitivity and specificity. Statistically significant differences and moderate correlation levels were observed for SII and NLR in predicting the anastomotic leak rate and degree of postoperative complications. No correlations were found between the LMR and PLR or the abovementioned outcomes. The MLP network analysis showed superior prediction value in terms of both sensitivity (0.78 ± 0.07; 0.74 ± 0.04; 0.71 ± 0.13) and specificity (0.81 ± 0.11; 0.69 ± 0.03; 0.9 ± 0.04) for all the given tasks. Preoperative SII and NLR appear to be modest prognostic factors for anastomotic leakage and overall morbidity. Using an artificial neural network offers superior prognostic results in the preoperative risk assessment for overall morbidity and anastomotic leak rate.