Composite scoring system and optimal tumor budding cut-off number for estimating lymph node metastasis in submucosal colorectal cancer

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.

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.

Adapting SAM to Histopathology Images for Tumor Bud Segmentation in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist's TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.

Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma

Current deep learning methods in histopathology are limited by the small amount of available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor budding quantification performed using H&E-stained slides is crucial for cancer staging and prognosis but is subject to labor-intensive annotation and human bias. Thus, acquiring a large-scale, fully annotated dataset for training a tumor budding (TB) segmentation/detection system is difficult. Here, we present a DatasetGAN-based approach that can generate essentially an unlimited number of images with TB masks from a moderate number of unlabeled images and a few annotated images. The images generated by our model closely resemble the real colon tissue on H&E-stained slides. We test the performance of this model by training a downstream segmentation model, UNet++, on the generated images and masks. Our results show that the trained UNet++ model can achieve reasonable TB segmentation performance, especially at the instance level. This study demonstrates the potential of developing an annotation-efficient segmentation model for automatic TB detection and quantification.

Effects of tumour budding on adjuvant chemotherapy in colorectal cancer

BACKGROUND

High tumour budding has been indicated as a risk factor of poor survival in colorectal cancer. This study aimed to investigate the impact of tumour budding grades and the use of adjuvant chemotherapy on prognosis in patients with colorectal cancer.

METHODS

This study included consecutive colorectal cancer patients who underwent radical surgery for primary colorectal adenocarcinoma at The Sixth Hospital of Sun Yat-sen University between 2009 and 2019. Tumour budding was assessed based on the recommendations of the International Tumor Budding Consensus Conference using haematoxylin and eosin (H&E)-stained slides with tumour samples. The primary outcome of interest was to correlate tumour budding with disease-free survival and overall survival; the secondary outcome was investigation of the impact of adjuvant therapy on different tumour budding grades. In addition, a subgroup analysis was performed for the effects of lymphocytic infiltration on adjuvant chemotherapy in patients with Bd3.

RESULTS

Of 709 eligible patients, 412 with colorectal cancer were included. According to the International Tumor Budding Consensus Conference, 210 (50.9 per cent), 127 (30.8 per cent) and 75 (18.2 per cent) were classified as low budding (Bd1), intermediate budding (Bd2) and high budding (Bd3) respectively. Patients with Bd1, Bd2 and Bd3 had 5-year disease-free survival rates of 82.9 per cent, 70.1 per cent and 49.3 per cent respectively, and 5-year overall survival rates of 90 per cent, 79.5 per cent and 62.7 per cent respectively (P <0.001). Adjuvant chemotherapy yielded a significant survival benefit in patients with Bd3 (5-year disease-free survival, 65 per cent versus 31.4 per cent, P <0.001; 5-year overall survival, 84.4 per cent versus 63.1 per cent, P <0.001), but not in those with Bd1 or Bd2. In patients with Bd3, the benefit of adjuvant chemotherapy was maintained in those with low, but not high lymphocytic infiltration.

CONCLUSION

High grade of tumour budding was strongly correlated with poorer survival outcomes in colorectal cancer. Patients with Bd3 benefited from adjuvant chemotherapy, with the exclusion of patients with high lymphocytic infiltration.