The measured distance between tumor cells and the peritoneal surface predicts the risk of peritoneal metastases and offers an objective means to differentiate between pT3 and pT4a colon cancer

Underdiagnosis of positive resection margins and synchronous peritoneal metastases in locally advanced colon cancer: histopathological reassessment of primary resection in the COLOPEC trial

The aim of this study was to perform histopathological reassessment of primary resections of locally advanced colon cancer (CC) within a randomized controlled trial, with specific focus on surgical margins and synchronous locoregional peritoneal metastases (SL-PM), and to provide learning points for both surgeons and pathologists. All histopathological slides of patients with c/pT4N0-2M0 or perforated CC included in the COLOPEC trial were reassessed and correlated with surgical reports. The COLOPEC trial originally determined the value of prophylactic hyperthermic intraperitoneal chemotherapy (HIPEC). Frequency of positive margins (R +), R + subtypes, and SL-PM and the association with 5-year peritoneal recurrence were analyzed. Histopathological slides of 199 patients were reassessed. R + was present in 28 patients (14.1%), of which 8 occurred at the site of adhesiolysis (originally classified as pT4a in 6). SL-PM was present in 11 cases (5.5%), of which 9 were not recognized or misclassified. Both R + and SL-PM were associated with 5-year peritoneal metastases in cox regression analysis (HR 2.38, 95% CI 1.12-5.04 and HR 5.98, 95% CI 2.69-13.29, respectively). Of 9 patients with peritoneal recurrences detected during re-exploration at 5-8 weeks after primary tumor resection for intended HIPEC, 5 demonstrated either R + and/or SL-PM. This study brings to light previously unnoticed but clinicopathologically relevant aspects of CC pathology retaining to underdetected SL-PM and new R + types. Underrecognition until now probably relates to the complexity of advanced CC specimens, poor communication between surgeons and pathologists, and the low incidence among high volumes of CC specimens. Trial registration: NCT02231086 (Clinicaltrials.gov).

Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing

As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.

The clinical significance of elastic lamina invasion in patients with pStage II colorectal cancer: a notable prognostic indicator

BACKGROUND

Some colorectal cancers (CRCs) are clinically diagnosed as cT4a with　serosal invasion (SI). However, the cT4a is most often underdiagnosed pathologically as pT3 without SI by hematoxylin-eosin (H&E) staining alone. Using Elastica van Gieson (EVG) staining, some pT3 tumors invade the elastic lamina (EL), which extends just below the serosal layer. Recently, EL invasion (ELI) has been described as a poor prognostic factor for disease-free survival (DFS) and overall survival (OS) in patients with pStage II CRC. However, its clinicopathological significance remains unclear due to the limited number of studies and poor understanding of ELI.

OBJECTIVE

This study investigated the association between the ELI and patient prognosis.

METHODS

After 1982, pathological diagnosis was routinely performed using H&E and EVG staining methods, and long-term follow up was performed until 2016. All clinicopathological features including ELI were prospectively registered into our computer and 569 patients with pStage II CRC were collected from the database. Based on the ELI status, pT3 was divided into three pathological categories: pT3ELI - was defined as pT3a, pT3ELI + as pT3b and unidentified EL (pT3EL -) as pT3u.

RESULTS

Using H&E staining alone, gross cT4a was most often pathologically underdiagnosed as pT3 (93.8%) and very rarely as pT4a, resulting in a large diagnostic discrepancy. Using EVG staining, 60.7% of the cT4a tumors were diagnosed as pT3b. The 10-year DFS and OS rates were similar for pT3a and pT3u patients. However, the 10-year DFS and OS rates of pT3b patients were significantly lower than those of pT3a patients (75.6% vs. 95.6%, p < 0.0001 and 58.4% vs. 70.6%, p = 0.0024, respectively) but did not differ from those of pT4a patients (70.6%, p = 0.5799 and 52.0%, p = 0.1116, respectively). Multivariate analysis revealed that the ELI was the strongest independent risk factor for recurrence and CRC-specific death (p < 0.0001).

CONCLUSIONS

A better understanding of the ELI allows us to reconsider the diagnostic discrepancy of serosal invasion, i.e., pT3b should be considered pT4a. The ELI-based subclassification of pT3 is expected to be incorporated into the TNM staging system in the future. The ELI is a notable prognostic indicator in patients with pStage II CRC.

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

OBJECTIVES

Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM.

METHODS

A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC).

RESULTS

The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model.

CONCLUSIONS

The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans.

CRITICAL RELEVANCE STATEMENT

The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans.

KEY POINTS

Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

Identification of an Objective Cut-Off Point to Define the Clinical Stage T4a in Colon Cancer

Introduction: The current state of pathology practice and the variability in diagnosing pT4a colon cancer have been underexplored in existing studies. Our objective was to establish a specific cutoff point to distinguish between the pathological stages of pT3 and pT4a in colon cancer. Methods: We conducted a cross-sectional study involving pT3 and pT4 (pN0-2, cM0) colon cancers, measuring the distance to the serosa. Patients were categorized and analyzed based on this distance and the peritoneal reaction, with the aim being to ascertain their prognostic implications. Results: A total of 384 patients were analyzed. Patients with a distance between the invading front of cancer and the serosa ≥ 1 mm without a peritoneal reaction exhibited a median survival of 118 months, contrasting the amount of 70 months for those with <1 mm plus peritoneal reaction. Only lengths <1 mm with peritoneal reaction showed a significant correlation with mortality (p < 0.001). Conclusion: Our study revealed that patients in whom neoplastic cells were less than 1 mm from the serosal surface, accompanied by a peritoneal reaction (hemorrhage, inflammation, neovascularization, fibrin), had significantly lower survival rates compared to those with more than 1 mm distance and without peritoneal response (70 vs. 118 months, p < 0.001). Hence, such cases should be considered within the pT4a stage.