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Objective:To explore the value of tumor stroma ratio (TSR) in non-small lung cancer (NSCLC) tissue in predicting the efficacy of tumor immunotherapy.Methods:The clinical and histopathological data of patients with stage ⅢB-Ⅳ NSCLC treated with immune checkpoint inhibitors in the Renmin Hospital of Wuhan University from January 2017 to December 2020 were collected. Taking 50% as the TSR boundary value, the patients were divided into low TSR group (≤50%) and high TSR group (>50%) . The histopathological features, 4-cycle objective response rate (ORR) and disease control rate (DCR) , 6-cycle ORR and DCR, and progression-free survival (PFS) were compared between the two groups. Univariate and multivariate Cox regression models were used to analyze the prognostic factors related to PFS.Results:A total of 50 patients were included, including 27 with low TSR and 23 with high TSR. There were no significant differences between the two groups in age ( χ2=0.59, P=0.441) , gender ( P=0.578) , smoking history ( χ2=0.12, P=0.730) , histopathological type ( χ2=2.33, P=0.313) , TNM stage ( χ2=0.22, P=0.636) , 4-cycle ORR ( χ2=0.48, P=0.487) and DCR ( P=0.593) , 6-cycle ORR ( χ2=0.05, P=0.818) and DCR ( P=0.641) . The incidence of brain metastasis was higher in the high TSR group than that in the low TSR group [34.8% (8/23) vs. 7.4% (2/27) , χ2=4.23, P=0.040]. Kaplan-Meier survival analysis showed that the PFS in the low TSR group was significantly longer than that in the high TSR group (15.6 months vs. 10.2 months, χ2=13.84, P<0.001) . Univariate analysis showed that TSR value ( HR=0.29, 95% CI: 0.14-0.58, P<0.001) and brain metastasis ( HR=2.38, 95% CI: 1.12-5.05, P=0.024) were correlated with the worse prognosis of NSCLC patients. Multivariate Cox regression analysis showed that TSR value was an independent prognostic factor for NSCLC immunotherapy ( HR=0.32, 95% CI: 0.14-0.70, P=0.004) . Conclusion:TSR is an independent predictor of immunotherapy for NSCLC, but whether it can predict the short-term efficacy of immunotherapy for advanced NSCLC still needs further research.
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Objective:To develop and validate a radiomics biomarker for the preoperative estimation of the tumor-stroma ratio (TSR) in rectal cancer.Methods:From January 2016 to March 2019, totally 149 patients with rectal cancer were enrolled retrospectively at Fudan University Shanghai Cancer Center. The patients were divided into two cohorts using a random number table, 119 in the training and 30 in validation cohorts. The patients were classified into the TSR-high group (TSR>50%) and TSR-low group (TSR≤50%) according to the content of tumor stroma in pathology. All patients underwent T 2WI, enhanced T 1WI and DWI. The lesions on the T 2WI, enhanced T 1WI, DWI and ADC images were delineated and radiomics features were extracted. A radiomics signature (rad-score) was generated by using the least absolute shrinkage and selection operator (LASSO) algorithm. The Spearman correlation coefficients were used to determine the association between rad-score and TSR. The area under the ROC curve (AUC) was used to assess the diagnostic performance of the rad-score. The reliability of the rad-score was quantified by calculating the sensitivity, specificity and accuracy of TSR. Results:With LASSO, a rad-score with 13 radiomics parameters was successfully constructed and was positively correlated with TSR score in the training ( r=0.72, P<0.001) and validation cohorts ( r=0.46, P=0.011). In the training cohort, the AUC of the rad-score was 0.940, with the sensitivity, specificity, accuracy of 100%, 87.3%, 92.4%. In the validation cohort, the AUC was 0.796, with the sensitivity, specificity, accuracy of 83.3%, 67.7%, 73.3%. Conclusions:The rad-score is of promising value for TSR estimation in rectal cancer. It is a promising supplement for patient stratification and may inform decision-making.
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Introduction: Several studies regarding tumor-stroma ratio (TSR) in colorectal, esophageal, breast, endometrial, and cervical carcinomas have been done in the past with significant results. Objectives: The objectives of this study were to (1) study and grade TSR in buccal mucosa and tongue squamous cell carcinoma (SCC), (2) grade inflammatory cell infiltrate surrounding the tumor, and (3) correlate the above two parameters with tumor grade, lymph node metastasis, lymphovascular invasion (LVI), and perineural invasion (PNI). Materials and Methods: Totally, 25 patients of buccal SCC and 16 cases of tongue SCC were included in the study. TSR was assessed visually on the hematoxylin and eosin-stained tissue sections by two independent observers. Cases were categorized into two groups: One with high TSR >50% (stroma poor) and the other with low TSR <50% as the stroma-rich group. TSR was correlated with tumor size, lymph node metastasis, inflammatory cell infiltrate, LVI, and PNI. Data were analyzed by the Statistical Package for the Social Sciences version 16.0 (Chicago, IL, USA) for Windows. The Chi-square and Fischer's exact tests were applied in the analysis of categorical variable. Results and Conclusion: SCC of buccal mucosa showed a significant correlation between TSR and size of the tumor (P = 0.001). We found that smaller the tumor size ≤2 cm (Stage T1), lesser the TSR, and size >2 cm was found to be associated with higher TSR. Hence, higher TSR (stroma poor) was associated with an adverse pathological characteristic, i.e., advanced T significantly. There was no significant correlation between TSR and inflammatory infiltrate with grade of the tumor, lymph node metastasis, LVI, and PNI. In 16 cases of SCC of the tongue; no correlation was observed between TSR and inflammatory infiltrate with tumor size, grade of the tumor, lymph node metastasis, LVI, and PNI. TSR has been studied in various malignancies (mostly adenocarcinomas) including laryngeal SCCs; however, it has never been studied on oral SCCs
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Aims and Objectives: We examined the prognostic value of Tumor stroma ratio (TSR) in breast tumor core biopsy (TCB) specimen to determine response to neoadjuvant therapy (NAT) prior to modified radical mastectomy (MRM). Methods: This was a retrospective analysis of patients with breast cancer who underwent TCB before NAT between August 2016 and July 2018. TSR in TCB was studied independently by 2 pathologists ( VM, VS) defined as stroma rich (TSR?50%) or stroma poor (TSR>50%). MRM specimen of these patients were subsequently studied .Residual cancer burden (RCB) was calculated using the MD Anderson RCB calculator, categorized as complete (0), good (1) Partial (2) and no response (3). Statistical analysis was done to assess correlation of TSR to RCB. Results: A total of 62 patients were analyzed. Mean(SD) age was 48(11) years.Twenty eight (45%) and 34 (55%) patients were stroma rich and stroma poor respectively. Twenty six (42%) patients were responders and 36 (58%) non-responders to NAT. Among stroma rich patients, only 3 (10%) were responders (Class 0 &1)and 25 (90%) non-responders(Class2&3)to NAT, among stroma poor patients 23 (68%) responded well and 11 (32%) did not.TSR had a moderate negative correlation with RCB (-0.6). On univariate analysis, only TSR had a significant effect on RCB class (<0.001). Conclusions: TSR on TCB is a useful prognostic factor to determine response of breast carcinoma patients to neoadjuvant therapy.It is cost effective, simple and quick. Larger multi-centric studies would be useful to study its clinical implications.
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Presently morphological evaluation and special staining scoring system are important components of basic and clinical research, and are very important for judging the efficacy of drugs and gene intervention. However, the current visual scoring system has some disadvantages such as strong subjectivity, poor repeatability and low accuracy, and is prone to missed diagnosis and misdiagnosis. Artificial intelligence technology based on deep learning is expected to overcome these problems. In our study, we found that the convolutional neural network can be used to accurately extract internal features related to the treatment and prognosis of tumors, such as tumor-stroma ratio, nerve invasion and spatial distribution of lymphatic cells in tumor specimens, visualizing and digitalizing the curative effect of drug intervention on disease progression, and can quantify and automatic evaluate the expression of molecular biomarkers related to clinical treatment, classification and prognosis. The application of artificial intelligence technology in tissue and cell morphology assessment will promote the consistency, repeatability and accuracy of clinical drug evaluation and basic scientific research evaluation, and is expected to further promote the development of medical research.
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Objective To investigate the value of tumor-stroma ratio(TSR) in evaluating the prognosis of patients with triple-negative breast cancer (TNBC).Methods A total of 87 patients with TNBC were selected from January 2010 to December 2014 in the Second People's Hospital of Yibin.The TSR was detected by Hematoxylin eosin staining,the patients with TSR ＜50％ were included in the stroma-rich group,and the patients with TSR≥50％ were included in the stroma-rare group.The influencing factors of tumor-free survival and overall survival in TNBC patients were analyzed by COX regression model.The effect of TSR on tumor-free survival and overall survival in TNBC patients was further analyzed by Kaplan-Meier method.Results Among the 87 TNBC patients,there were 38 patients with TSR≥50％ (stroma-rare group) and 49 patients with TSR ＜ 50％ (stroma-rich group).The 5-year tumor-free survival rate and overall survival rate of the 87 TNBC patients was 52.9％ (46/87) and 56.3％ (49/87) respectively.Single factor COX regression analysis showed that the tumor diameter staging,lymph node metastasis,the number of tumors and TSR were significantly correlated with the overall survival rate and tumor-free survival rate of patients with TNBC (P ＜ 0.05).Multivariate COX regression analysis showed that the tumor diameter staging,lymph node metastasis,the number of tumors and TSR were independent risk factors for overall survival rate and tumor-free survival rate of patients with TNBC (P ＜ 0.05).Kaplan-Meier survival analysis showed that the tumor-free survival rate and the overall survival rate of TNBC patients in the stroma-rare group were 68.4％ (26/38) and 73.7％ (28/38),and they were 40.8％ (20/49) and 42.9％ (21/49) in the stroma-rich group.The Log-Rank test showed that the overall survival rate and disease-free survival rate of the TNBC patients in the stroma-rare group were significantly higher than those in the stroma-rich group (x2 =7.788,9.799;P ＜ 0.05).Conclusion TSR is significantly associated with the prognosis of TNBC patients,and it is an independent risk factor for the prognosis of TNBC patients.TSR can be used as an indicator for evaluating the prognosis of TNBC patients.
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Tumor-stroma ratio is the ratio of tumor cells and stromal tissue. It can be evaluated through HE-stained sections. Usually a 50% cut-off value is taken. TSR is stratified as stroma rich(TSR < 50% )and stroma poor(TSR≥50% ). The different prognosis between the two groups can be assessed. Several previous studies show that TSR is an independent prognostic factor for colon carcinoma,breast cancer,esophageal squa-mous cell carcinoma,cervical carcinoma and non-small cell lung cancer;patients with stroma-rich tumor suffer worse prognosis. The cause might be that tumor cells could activate stroma cells while activated stroma cells could promote the growth,infiltration and metastasis of tumors. And tumor stroma should be recognized as an indispensible participant in progression and invasion of carcinoma.