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

Abstract

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.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

“…The ML model based on extreme gradient boosting (XGB) was selected in our study because of its generalizability, low risk of overfitting, high interpretability [25], and high scalability [34]. XGB has been confirmed to be a reliable method for recognizing patterns in other diseases such as lupus erythematosus [16], traumatic brain injury-induced coagulopathy [35], epilepsy [36], diabetes [37], Alzheimer's disease [38,39], HIV [40,41], or different types of cancer [42][43][44][45][46]. We, therefore, used the aforementioned ML technique to determine which factors were most predictive of disease severity in a closed group of patients hospitalized for COVID-19 during the first two months of the pandemic, a time when the population did not yet have herd immunity and had not yet been vaccinated.…”

Reassessing Breast Cancer-Associated Fibroblasts (CAFs) Interactions with Other Stromal Components and Clinico-Pathologic Parameters by Using Immunohistochemistry and Digital Image Analysis (DIA)

“…Its diagnostic and prognostic value increased by association of tumor stroma ratio with other clinic-pathologic parameters such as multiparametric MRI findings [43], oncogenic proteins [44], axillary lymph node metastasis [45], or TNM staging [46]. The tumor-stroma ratio was evaluated using different DIA software, but it seems that one of the favorites is the QuPath analysis platform due to its facilities covering a wide spectrum of needs for researchers who assess microscopic images with an emphasis on tumor stroma [38,39,47]. We also chose the QuPath platform due to its tools' ability to sharply detect and label stromal fibroblasts and automatically perform stromal scoring and H-Score.…”

Significance of Tumor–Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors

“…All data were extracted from 51 eligible studies [ 2 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. Extracted data included the author’s information, study location, number of patients analyzed, and high TSR evaluation criteria.…”

“…However, the impact of the proportion of stroma is not clear in terms of whether it accelerates or suppresses tumor progression. Recently, the prognostic implications of TSR have been exhibited for various malignant tumors [ 2 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. In colorectal cancers, low stroma was associated with less frequent vascular and perineural invasion and distant metastasis [ 45 ].…”

“…TSR is divided into high TSR (stroma-low) and low TSR (stroma-high) by evaluation criteria. In previous studies, the evaluation criteria of TSR affected high TSR rates [ 2 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. If the evaluation criteria are different, the prognostic implications of TSR can differ.…”