Digital image analysis provides robust tissue microenvironment-based prognosticators in stage I-IV colorectal cancer patients

Prognostic Significance of Tumor-associated Stroma in Nasopharyngeal Carcinoma: A Multicenter Study

Assessment of tumor-associated stroma has shown a reliable prognostic value in recent research. We evaluated the prognostic value of tumor-stroma ratio (TSR) in a large multicenter cohort of nasopharyngeal carcinoma (NPC). We used the conventional hematoxylin and eosin-stained slides of 115 cases of NPC to assess TSR as described in recent guidelines. The amount of tumor-associated stroma was assessed as a percentage and then tumors were classified as stroma-high (>50%) or stroma-low (≤50%). Kaplan-Meier curves, χ 2 test, and Cox regression univariable and multivariable analyses were carried out. A total of 48 (41.7%) tumors were stroma-high and 67 (58.3%) tumors were stroma-low. In the Cox regression multivariable analysis, the tumors categorized as stroma-high were associated with a worse overall survival with a hazard ratio of 2.30 (95% CI: 1.27-4.15, P =0.006) and with poor disease-specific survival (hazard ratio=1.87, 95% CI: 1.07-3.28, P =0.029). The assessment of TSR in NPC is simple and cost-effective, and it has a significant prognostic value. TSR can aid in risk stratification and clinical decision-making in NPC.

Tumor-Stroma Ratio is a Promising Prognostic Classifier in Oropharyngeal Cancer

Tumor-stroma ratio (TSR) has been analyzed in many tumor types. To date, the clinical significance of TSR has not been investigated in oropharyngeal squamous cell carcinoma (OPSCC). We used a recently introduced recommendation for the assessment of TSR in a large cohort of 182 patients with OPSCC treated at the Helsinki University Hospital. The percentage of tumor-associated stroma was estimated in hematoxylin and eosin (HE)-stained sections and categorized into 2 groups: "stroma-high" (>50%) and "stroma-low" (≤50%). In multivariable analysis, TSR had a significant association with patient survival as stroma-high tumors showed worse disease-free survival (hazard ratio [HR] = 3.22, 95% confidence interval [CI] = 1.43-7.26, P = .005), disease-specific survival (HR = 2.48, 95% CI = 1.29-4.74, P = .006), and overall survival (HR = 2.23, 95% CI = 1.29-3.85, P = .004). The prognostic value of TSR was superior to the Tumor-Node-Metastasis classification. In addition, the significant prognostic value of TSR was demonstrated when analyzing human papillomavirus (HPV)-positive and HPV-negative cases separately (P < .05). In conclusion, TSR is a powerful prognostic indicator in OPSCC. It can be assessed quickly without additional costs using standard HE slides. Owing to its simplicity and reproducibility, TSR can be implemented in routine pathology diagnostics and reporting. Patients with stroma-rich tumors have an increased risk of recurrence and cancer-related mortality and may benefit from appropriate intensive treatment strategies with close follow-up.

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.