Tumor-stroma Ratio Research Articles

Stroma-and Tumor-Associated Predictive Features inSalivary Gland Adenoid Cystic Carcinoma of the Headand Neck.

There is lack of knowledge on the utility of prognostic histopathologic characteristics in adenoid cystic carcinoma (ACC) of the head and neck. We evaluated the prognostic value of tumor and stroma-related histopathologic features in ACC. A total of 65 cases of ACC from minor and major salivary glands were included in this study. We evaluated tumor budding, tumor-infiltrating lymphocytes (TILs), and tumor-stroma ratio (TSR) in hematoxylin and eosin (HE) stained sections. Stroma-rich ACCs recurred more frequently (p = 0.029) during follow-up and associated with distant metastasis (p = 0.038). In multivariable analysis, stroma-rich tumors associated with poorer disease-specific survival with a hazard ratio of 3.76 (95% CI 1.10-12.83, p = 0.034). ACCs commonly showed a low infiltration of TILs as 89% of the tumors was characterized by an immune desert pattern. Low infiltration of TILs associated significantly with increased tumor budding (p = 0.039). Adverse features of TSR and tumor budding are widely expressed in ACC, and stroma-rich tumors are associated with poor prognosis. Low number of TILs in ACC tissue indicates a weak immune response by the host and illustrates the nature of ACC as a relentless malignancy.

Prognostic Role of Tumor-Infiltrating Lymphocytes, Tumor Budding, Tumor Border Configuration, and Tumor Stroma Ratio in Colorectal Carcinoma

Prognostic Role of Tumor-Infiltrating Lymphocytes, Tumor Budding, Tumor Border Configuration, and Tumor Stroma Ratio in Colorectal Carcinoma

Inverse correlation between the amounts of lymphocytic infiltrate and stroma in breast carcinoma

BackgroundPrevious breast carcinoma studies focused on the evaluation of tumour-infiltrating lymphocytes (TILs) or of tumoural stroma via the tumour stroma ratio (TSR). Few studies assessed peritumoural lymphocytes and almost no studies investigated a possible relationship between lymphocytes and stroma. This prompted us to evaluate the amount of tumour cells, intra- and peritumoural lymphocytes, and stroma in breast cancer to support the hypothesis that the stroma may block the infiltration of lymphocytes inside the tumour. MethodsWe collected a retrospective series of 158 breast cancers (<25 mm). In addition to standard TILs and TSR evaluations, we assessed the percentages of tumour cells, stromal myofibroblasts, intra- and peritumoural lymphocytes on full-section tumours with haematoxylin and eosin and immunohistochemical staining. ResultsWe showed significant negative correlations between the amounts of stroma and both intra- and peritumoural lymphocyte percentages. Considering the estrogen receptor positive invasive breast cancer of no special type cases, we showed that TSR had a positive prognostic value with an optimal threshold of 10%. ConclusionsThis study is one of the first to show inverse correlations between tumoural stroma amount and intra- and peritumoural lymphocyte percentages, which supports the hypothesis that tumoural stroma can prevent the recruitment of lymphocytes around and within the tumour.

Prognostic Significance of Tumor-Stroma Ratio (TSR) in Head and Neck Squamous Cell Carcinoma: Systematic Review and Meta-Analysis.

The management of head and neck squamous cell carcinoma (HNSCC) relies heavily on TNM staging and WHO histologic grading; however, in recent years, the analysis of prognostic markers expressed in the tumor stroma has gained attention. The tumor-stroma ratio (TSR) quantifies the proportion of tumor tissue relative to the surrounding stromal tissue; it is assessed with the percentage of stromal tissue within the tumor area, with a cutoff point of 50% being widely used to discriminate high-stroma cancer. In this systematic review and meta-analysis, we investigated the potential prognostic role of the TSR in HNSCC. After a literature screening, 24 studies dealing with the TSR and survival outcomes were included. The TSR showed a significant association with overall survival (OS) in both unadjusted and adjusted measures (RR 2.04, CI 1.57-2.65, p < 0.01; HR 2.36 CI 1.89-2.94, p < 0.00001), with an even stronger prognostic potential in oral cavity/oral tongue cancers (RR 2.44 CI 1.84-3.22, p < 0.00001). The TSR also showed prognostic value when dealing with cancer-specific survival and was associated with a reduction in disease-free survival (DFS). In particular, the TSR also retained its prognostic role in terms of DFS when specifically considering early-stage cancers in both unadjusted and adjusted analyses (RR 1.81 CI 1.57-2.10, p < 0.00001; HR 2.09 CI 1.58-2.76, p < 0.00001). Therefore, we conclude that the TSR is a reliable prognostic marker that is easy to assess in routine histological slides and can be effectively implemented in the routine evaluation of HNSCC.

Serous Ovarian Carcinoma: Detailed Analysis of Clinico-Pathological Characteristics as Prognostic Factors.

Serous ovarian carcinoma (SOC) is the most common subtype of epithelial ovarian cancer, with high-grade (HGSOC) and low-grade (LGSOC) subtypes presenting distinct clinical behaviours. This study aimed to evaluate histopathologic features in SOC, correlating these with prognostic outcomes, and explore the potential clinical implications. We analysed 51 SOC cases for lymphovascular space invasion (LVSI), tumour border configuration (TBC), microvessel density (MVD), tumour budding (TB), the tumour-stroma ratio (TSR), the stromal type, tumour-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs). A validation cohort of 54 SOC cases from The Cancer Genome Atlas (TCGA) was used for comparison. In the discovery set, significant predictors of aggressive behaviour included LVSI, high MVD, high TB, and low TILs. These findings were validated in the validation set where the absence of TLSs, lower peritumoural TILs, immature stromal type, and low TSR were associated with worse survival outcomes. The stromal type was identified as an independent prognostic predictor in SOC across both datasets. Inter-observer variability analysis demonstrated substantial to almost perfect agreement for these features, ensuring the reproducibility of the findings. The histopathological evaluation of immune and stromal features, such as TILs, TLSs, TB, TSR, and stromal type, provides critical prognostic information for SOC. Incorporating these markers into routine pathological assessments could enhance risk stratification and guide treatment, offering practical utility, particularly in low-resource settings when molecular testing is not feasible.

Associations between Dynamic-Contrast enhanced MRI with histopathological features in cholangiocarcinoma.

The associations between histopathology and imaging remain elusive and investigating underlying reasons of tumor microstructure resulting in an imaging phenotype is of clinical importance. The present study used cross-sectional guided biopsy specimen to employ the exact spatial biopsy localization to correlate the prebioptic magnetic resonance imaging (MRI) with immunohistochemical stainings of the histopathological specimen. Twenty-seven patients with mass-forming cholangiocarcinoma (CCA) were included in the present analysis. All patients were imaged by a 1.5 T clinical scanner at last one month before the biopsy. The contrast enhanced dynamic sequences were analyzed with quantified signal intensities. The bioptic specimen were obtained by cross-sectional guided biopsy and was further analyzed with cell density, proliferation index (Ki67), tumor-infiltrating lymphocytes, tumor-stroma ratio as well as Collagen. There were no statistically significant correlations between the signal intensities of the MRI with cell count, tumor-stroma ratio, Ki67 index and CD45 count. Only a moderate correlation was identified between relative signal intensities of the venous phase with the Collagen-stained area (r=0.40, p=0.04). DCE MRI is not associated with histopathology features in CCA. The complex interactions of the tumor and the tumor micromilieu are not reflected by the MRI phenotype.

One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.

To develop a contrastive language-image pretraining (CLIP) model based on transfer learning and combined with self-attention mechanism to predict the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma on preoperative enhanced CT images, in order to understand the biological characteristics of tumors for risk stratification and guiding feature fusion during artificial intelligence-based model representation. This retrospective study collected a total of 207 PDAC patients from three hospitals. TSR assessments were performed on surgical specimens by pathologists and divided into high TSR and low TSR groups. This study developed one novel CLIP-adapter model that integrates the CLIP paradigm with a self-attention mechanism for better utilizing features from multi-phase imaging, thereby enhancing the accuracy and reliability of tumor-stroma ratio predictions. Additionally, clinical variables, traditional radiomics model and deep learning models (ResNet50, ResNet101, ViT_Base_32, ViT_Base_16) were constructed for comparison. The models showed significant efficacy in predicting TSR in PDAC. The performance of the CLIP-adapter model based on multi-phase feature fusion was superior to that based on any single phase (arterial or venous phase). The CLIP-adapter model outperformed traditional radiomics models and deep learning models, with CLIP-adapter_ViT_Base_32 performing the best, achieving the highest AUC (0.978) and accuracy (0.921) in the test set. Kaplan-Meier survival analysis showed longer overall survival in patients with low TSR compared to those with high TSR. The CLIP-adapter model designed in this study provides a safe and accurate method for predicting the TSR in PDAC. The feature fusion module based on multi-modal (image and text) and multi-phase (arterial and venous phase) significantly improves model performance.

Tumor microenvironment characteristics association with clinical outcome in patients with resected intestinal-type gastric cancer.

Tumor microenvironment (TME) characteristics including tumor stroma ratio (TSR), tumor budding (TB), and tumor-infiltrating lymphocytes (TILs) were examined in resected gastric cancer. These TME features have been shown to indicate metastatic potential in colon cancer, and intestinal-type gastric cancer (IGC) has pathological similarities with that malignancy. TSR, TB, and TILs were quantified in routine histological sections from 493 patients with IGC who underwent radical resection at 2 university hospitals in China from 2010 to 2016. TME variables were dichotomized as follows: TSR (50%), TILs (median), TB per international guidelines (4 buds/0.785mm2), and platelet-lymphocyte ratio (PLR) per survival ROC. Association of TME features with patient clinicopathological characteristics, time-to-recurrence (TTR), and cancer-specific-survival (CSS) were examined using univariate and multivariate analysis, including a relative contribution analysis by Cox regression. Patients whose tumors showed high TSR or high TB or low TILs were each significantly associated with increased T and N stage, higher histological grade, and poorer TTR and CSS at 5 years. Only TSR and N stage were independently associated with TTR and CSS after adjustment for covariates. PLR was only independently associated with TTR after adjustment for covariates. Among the variables examined, only TSR was significantly associated with both TTR (HR 1.72, 95% CI, 1.14-2.60, P = .01) and CSS (HR 1.62, 95% CI, 1.05-2.51, P = .03) multivariately. Relative contribution to TTR revealed that the top 3 contributors were N stage (45.1%), TSR (22.5%), and PLR (12.9%), while the top 3 contributors to CSS were N stage (59.9%), TSR (14.7%), and PLR (10.9%). Among the examined TME features, TSR was the most robust for prognostication and was significantly associated with both TTR and CSS. Furthermore, the relative contribution of TSR to patient TTR and CSS was second only to nodal status.

Tumor Microenvironment-Based Risk Stratification of Oropharyngeal Squamous Cell Carcinoma.

Evaluation of the prognostic impact of tumor microenvironment (TME) has received attention in recent years. We introduce a TME-based risk stratification for oropharyngeal squamous cell carcinoma (OPSCC). A total of 182 patients treated for OPSCC at the Helsinki University Hospital were included. TME-based risk stratification was designed combining tumor-stroma ratio and stromal tumor-infiltrating lymphocytes assessed in hematoxylin and eosin-stained sections. In multivariable analysis, TME-based risk stratification associated with poor disease-free survival with a hazard ratio (HR) of 2.68 (95% CI 1.11-6.48, p = 0.029). In addition, the proposed risk stratification was associated with poor disease-specific survival (HR 2.687, 95% CI 1.28-5.66, p = 0.009) and poor overall survival (HR 2.21, 95% CI 1.23-3.99, p = 0.008). Our TME-based risk stratification provides a powerful prognostic tool that can be used in daily treatment planning of OPSCC together with tumor-related prognostic markers.

Mesenchymal stem cells in tumor microenvironment: drivers of bladder cancer progression through mitochondrial dynamics and energy production

Mesenchymal stem cells (MSCs) in tumor microenvironment (TME) are crucial for the initiation, development, and metastasis of cancer. The impact and mechanism of MSCs on bladder cancer are uncertain. Here we analyzed 205 patient samples to explore the relationships between tumor-stroma ratio and clinicopathological features. A co-culture model and nude mouse transplantation were used to explore the biological roles and molecular mechanisms of MSCs on bladder cancer cells. We found that a high tumor-stroma ratio was significantly associated with a larger tumor size and higher T stage, pathological grade, number of vascular invasions, and poor overall survival. MSCs in TME promoted the ability of bladder cancer cells to proliferate, migrate, and invade in vitro and in vivo. Next, we demonstrated that MSCs enhance mitochondrial autophagy and mitochondrial biogenesis of bladder cancer cells, and increase energy production, thereby promoting bladder cancer cell progression. Kynurenine (Kyn) produced by MSCs could enhance mitochondrial function by activating the AMPK pathway. IDO1 inhibitor could reverse the tumor‑promoting effects of MSCs in vitro and in vivo. Our results demonstrated that tryptophan metabolites Kyn of MSCs in TME could enhance mitochondrial function by activating the AMPK pathway, thereby promoting bladder cancer cell progression.

The prognostic significance of growth pattern, tumor budding, poorly differentiated clusters, desmoplastic reaction pattern and tumor-stroma ratio in colorectal cancer and an evaluation of their relationship with KRAS, NRAS, BRAF mutations

Growth pattern (GP), tumor budding (TB), poorly differentiated clusters (PDC), desmoplastic reaction pattern (DRP) and tumor-stroma ratio (TSR) are prognostic histomorphological parameters in colorectal cancer (CRC). Correlations between these parameters, their individual prognostic values, and their relationship with KRAS/NRAS/BRAF mutations have not been comprehensively examined. We aimed to investigate these associations, which have not been previously explored in this combination. 126 CRC cases were included. GP, TB, PDC, DRP and TSR were evaluated by two experienced pathologists. KRAS/NRAS/BRAF mutation profile were determined using qPCR. Demographic, clinicopathological and survival data were recorded. Interrelations were investigated by statistical analysis. Infiltrative GP was more frequent in high-score TB, PDC-G3, and stroma-high tumors (p < 0.05). High-score TB was more common in PDC-G3 and stroma-high tumors (p < 0.05). Immature DRP was more frequent in stroma-high tumors (p = 0.014). Among histomorphological parameters, a significant relationship was found only between infiltrative GP and the presence of KRAS mutation (p = 0.023). Moreover, GP was significantly associated with pT, lymphatic invasion, perineural invasion (p < 0.05). Effects on survival were assessed using Kaplan-Meier method and Cox proportional hazards model. TB and PDC were identified as independent predictors of overall survival. Higher TB score (p = 0.008) and higher PDC grade (p = 0.013) lead to worse survival. Interestingly, GP, DRP, TSR or KRAS/NRAS/BRAF mutations were not associated with overall survival. Our results highlight the prognostic significance of TB and PDC. We suggest incorporating TB and PDC into routine CRC reports. The association of KRAS mutation with infiltrative GP supports its role in the acquisition of invasive behavior.

Are the tumor microenvironment characteristics of pretreatment biopsy specimens of colorectal cancer really effectively predict the efficacy of neoadjuvant therapy: A retrospective multicenter study.

More and more studies had pointed out that the tumor microenvironment characteristics based on colorectal cancer (CRC) pretreatment biopsy specimens could effectively predict the efficacy of neoadjuvant therapy, but under hematoxylin and eosin (HE) staining, whether the tumor microenvironment characteristics observed by pathologists could predict the efficacy of neoadjuvant therapy remains to be discussed. We collected 106 CRC patients who received neoadjuvant treatment and surgical resection from 3 hospitals. The number of mitosis, inflammation degree, desmoplastic reaction (DR), necrosis, tumor-stroma ratio (TSR) and tumor budding (TB) of CRC pretreatment biopsy specimens were observed under HE staining, and the degree of tumor pathological remission of CRC surgical specimens after neoadjuvant treatment was evaluated. According to the tumor regression grade (TRG), patients were divided into good-responders (TRG 0-1) and non-responders (TRG 2-3). All data were analyzed with SPSS software (version 23.0) to evaluate the correlation between the number of mitosis, inflammation degree, DR, necrosis, TSR and TB in pretreatment biopsy samples and the treatment effect. In univariate analysis, mitosis (P = .442), inflammation degree (P = .951), DR (P = .186), necrosis (P = .306), TSR (P = .672), and TB (P = .327) were not associated with the response to neoadjuvant therapy. However, we found that for colon cancer, rectal cancer was more likely to benefit from neoadjuvant therapy (P = .024). In addition, we further analyzed the impact of mitosis, inflammation degree, DR, necrosis, TSR and TB on neoadjuvant therapy in rectal cancer, and found that there was no predictive effect. By analyzing the characteristics of tumor microenvironment of CRC pretreatment biopsy specimens under HE staining, such as mitosis, inflammation degree, DR, necrosis, TSR and TB, it was impossible to effectively predict the efficacy of neoadjuvant therapy for CRC.

Digital Whole Slide Image Analysis of Elevated Stromal Content and Extracellular Matrix Protein Expression Predicts Adverse Prognosis in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis and limited treatment options. This study evaluates the prognostic value of stromal markers in TNBC, focusing on the tumor-stroma ratio (TSR) and overall stroma ratio (OSR) in whole slide images (WSI), as well as the expression of type-I collagen, type-III collagen, and fibrillin-1 on tissue microarrays (TMAs), using both visual assessment and digital image analysis (DIA). A total of 101 female TNBC patients, primarily treated with surgery between 2005 and 2016, were included. We found that high visual OSR correlates with worse overall survival (OS), advanced pN categories, lower stromal tumor-infiltrating lymphocyte count (sTIL), lower mitotic index, and patient age (p < 0.05). TSR showed significant connections to the pN category and mitotic index (p < 0.01). High expression levels of type-I collagen (>45%), type-III collagen (>30%), and fibrillin-1 (>20%) were linked to significantly worse OS (p = 0.004, p = 0.013, and p = 0.005, respectively) and progression-free survival (PFS) (p = 0.028, p = 0.025, and p = 0.002, respectively), validated at the mRNA level. Our results highlight the importance of stromal characteristics in promoting tumor progression and metastasis and that targeting extracellular matrix (ECM) components may offer novel therapeutic strategies. Furthermore, DIA can be more accurate and objective in evaluating TSR, OSR, and immunodetected stromal markers than traditional visual examination.

Research status of the tumor stroma ratio in prognosis and treatment of pancreatic cancer

An increasing number of studies suggested that the tumor microenvironment exerts a substantial influence on the pathophysiology of pancreatic cancer. As a crucial component of the tumor microenvironment,the tumor stroma plays a pivotal role in the occurrence,development,and chemotherapy resistance of pancreatic cancer. By serving as a proxy for the interaction between tumor cells and the microenvironment,the tumor stroma ratio(TSR) has emerged as a focal point of investigation in recent years. At present,numerous studies show that a low TSR is a protective factor for the prognosis of resectable pancreatic cancer. Additionally, patients with a low TSR are more suitable for the gemcitabine and albumin-bound paclitaxel chemotherapy regimen. But these researches are not conclusive, and there is still a gap between guiding precision treatment. Further research and exploration are required. Integration of artificial intelligence deep learning models into traditional pathological and imaging assessments facilitates precise evaluation of the TSR. It can also enable stratification and precision treatment of pancreatic cancer patients based on this index.

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model’s predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892–0.903, 0.710–0.894, and 0.850–0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study

Tumor–stroma ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathologic relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parameterization of the stromal landscape and validate TSR scores generated by our AI model against those assessed by humans. One hundred twelve MCC cases with whole-slide images were collected from 4 different institutions. Whole-slide images were first partitioned into 128 × 128-pixel “mini-patches,” then classified using a novel framework, termed pre-tumor and stroma (Pre-TOAST) and TOAST, whose output equaled the probability of the minipatch representing tumor cells rather than stroma. Hierarchical random samplings of 50 minipatches per region were performed throughout 50 regions per slide. TSR and tumor–stroma landscape (TSL) parameters were estimated using the maximum-likelihood algorithm. Receiver operating characteristic curves showed that the area under the curve value of Pre-TOAST in discriminating classes of interest including tumor cells, collagenous stroma, and lymphocytes from nonclasses of interest including hemorrhage, space, and necrosis was 1.00. The area under the curve value of TOAST in differentiating tumor cells from related stroma was 0.93. MCC stroma was categorized into TSR high (TSR ≥ 50%) and TSR low (TSR < 50%) using both AI- and human pathology–based methods. The AI-based TSR-high subgroup exhibited notably shorter metastasis-free survival (MFS) with a statistical significance of P = .029. Interestingly, pathologist-determined TSR subgroups lacked statistical significance in recurrence-free survival, MFS, and overall survival (P > .05). Density-based spatial clustering of applications with noise analysis identified the following 2 distinct TSL clusters: TSL1 and TSL2. TSL2 showed significantly shorter recurrence-free survival (P = .045) and markedly reduced MFS (P < .001) compared with TSL1. TSL classification appears to offer better prognostic discrimination than traditional TSR evaluation in MCC. TSL can be reliably calculated using an AI-based classification framework and predict various prognostic features of MCC.

The tumor-stroma ratio in giant cell tumor of bone: associations with the immune microenvironment and responsiveness to denosumab treatment

BackgroundCurrently, there is limited understanding regarding the clinical significance of the tumor-stroma ratio (TSR) in giant cell tumor of bone (GCTB). Hence, we aimed to investigate the distribution of TSR in GCTB and explore its correlation with various clinicopathologic factors, immune microenvironment, survival prognosis, and denosumab treatment responsiveness.MethodsWe conducted a multicenter cohort study comprising 426 GCTB patients treated at four centers. TSR was evaluated on hematoxylin and eosin-stained and immunofluorescent sections of tumor specimens. Immunohistochemistry was performed to assess CD3+, CD4+, CD8+, CD20+, PD-1+, PD-L1+, and FoxP3+ TIL subtypes as well as Ki-67 expression levels in 426 tissue specimens. These parameters were then analyzed for their correlations with patient outcomes [local recurrence-free survival (LRFS) and overall survival (OS)], clinicopathological features, and denosumab treatment responsiveness.ResultsLow TSR was significantly associated with poor LRFS and OS in both cohorts. Furthermore, TSR was also correlated with multiple clinicopathological features, TIL subtype expression, and denosumab treatment responsiveness. TSR demonstrated similar predictive capabilities as the conventional Campanacci staging system for predicting patients' LRFS and OS.ConclusionThe results of this study provide evidence supporting the use of TSR as a reliable prognostic tool in GCTB and as a predictor of denosumab treatment responsiveness. These findings may aid in developing individualized treatment strategies for GCTB patients in the future.

Combined expert-in-the-loop—random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments

BackgroundThe tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially accelerate TME analysis. The aims of this study were to (1) assess the feasibility of using hematoxylin and eosin (H&E)-stained whole slide images (WSI) to develop an AI model for evaluating the TME and (2) to characterize the TME of adenocarcinoma (ADCA) and squamous cell carcinoma (SCCA) in fibrotic and non-fibrotic lung.MethodsThe cohort was derived from chest CT scans of patients presenting with lung neoplasms, with and without background fibrosis. WSI images were generated from slides of all 76 available pathology cases with ADCA (n = 53) or SCCA (n = 23) in fibrotic (n = 47) or non-fibrotic (n = 29) lung. Detailed ground-truth annotations, including of stroma (i.e., fibrosis, vessels, inflammation), necrosis and background, were performed on WSI and optimized via an expert-in-the-loop (EITL) iterative procedure using a lightweight [random forest (RF)] classifier. A convolution neural network (CNN)-based model was used to achieve tissue-level multiclass segmentation. The model was trained on 25 annotated WSI from 13 cases of ADCA and SCCA within and without fibrosis and then applied to the 76-case cohort. The TME analysis included tumor stroma ratio (TSR), tumor fibrosis ratio (TFR), tumor inflammation ratio (TIR), tumor vessel ratio (TVR), tumor necrosis ratio (TNR), and tumor background ratio (TBR).ResultsThe model’s overall classification for precision, sensitivity, and F1-score were 94%, 90%, and 91%, respectively. Statistically significant differences were noted in TSR (p = 0.041) and TFR (p = 0.001) between fibrotic and non-fibrotic ADCA. Within fibrotic lung, statistically significant differences were present in TFR (p = 0.039), TIR (p = 0.003), TVR (p = 0.041), TNR (p = 0.0003), and TBR (p = 0.020) between ADCA and SCCA.ConclusionThe combined EITL—RF CNN model using only H&E WSI can facilitate multiclass evaluation and quantification of the TME. There are significant differences in the TME of ADCA and SCCA present within or without background fibrosis. Future studies are needed to determine the significance of TME on prognosis and treatment.

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer.

This study aims to introduce an innovative multi-step pipeline for automatic tumor-stroma ratio (TSR) quantification as a potential prognostic marker for pancreatic cancer, addressing the limitations of existing staging systems and the lack of commonly used prognostic biomarkers. The proposed approach involves a deep-learning-based method for the automatic segmentation of tumor epithelial cells, tumor bulk, and stroma from whole-slide images (WSIs). Models were trained using five-fold cross-validation and evaluated on an independent external test set. TSR was computed based on the segmented components. Additionally, TSR's predictive value for six-month survival on the independent external dataset was assessed. Median Dice (inter-quartile range (IQR)) of 0.751(0.15) and 0.726(0.25) for tumor epithelium segmentation on internal and external test sets, respectively. Median Dice of 0.76(0.11) and 0.863(0.17) for tumor bulk segmentation on internal and external test sets, respectively. TSR was evaluated as an independent prognostic marker, demonstrating a cross-validation AUC of 0.61±0.12 for predicting six-month survival on the external dataset. Our pipeline for automatic TSR quantification offers promising potential as a prognostic marker for pancreatic cancer. The results underscore the feasibility of computational biomarker discovery in enhancing patient outcome prediction, thus contributing to personalized patient management.