Histogram parameters derived from T1 and T2 weighted images correlate with tumor infiltrating lymphocytes and tumor-stroma ratio in head and neck squamous cell cancer

Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study

BACKGROUND

To investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC).

METHODS

The study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing the entire tumor, a total of 750 radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted (ceT1WI) imaging. To reduce dimensionality, reproducibility analysis by two radiologists and collinearity analysis were performed. The top six features were selected from each sequence alone, as well as their combination, using the minimum-redundancy maximum-relevance algorithm. Random forest, logistic regression, and support vector machine models were used to predict TIL levels in OTSCC, and 10-fold cross-validation was employed to assess the performance of the classifiers.

RESULTS

Based on the features selected from each sequence alone, the ceT1WI models outperformed the T2WI models, with a maximum area under the curve (AUC) of 0.820 versus 0.754. When combining the two sequences, the optimal features consisted of one T2WI and five ceT1WI features, all of which exhibited significant differences between patients with low and high TILs (all P < 0.05). The logistic regression model constructed using these features demonstrated the best predictive performance, with an AUC of 0.846 and an accuracy of 80.9%.

CONCLUSIONS

ML-based T2WI and ceT1WI radiomics can serve as valuable tools for determining the level of TILs in patients with OTSCC.

Multiperspective quantitative tumor-stroma ratio reveals histological areas associated with poor outcomes in oral squamous cell carcinoma

AIMS

Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological characteristics, and the individual histological characteristics of the tumors are poorly understood. Therefore, calculating the proportion of tumor cells in different regions that allow assessment of the prognostic outcomes for OSCC patients would be of great clinical significance.

METHODS AND RESULTS

We established an open-source software-based analytic pipeline that defines the inner tumor and invasive tumor front (ITF) in pancytokeratin-stained whole slide images (WSIs) and quantifies the tumor-stroma ratio (TSR) within the two regions. We applied this method to 114 patients with OSCC and predicted patient prognosis by the TSR. The proportion of tumor area in the inner tumor was generally higher than that in the ITF (p < 0.0001). TSR was an independent prognostic factor for overall survival (OS) (p = 0.016), disease-free survival (DFS) (p = 0.026), and relapse-free survival (RFS) (p = 0.037) in inner tumor, and TSR was an independent prognostic factor for OS (p = 0.00052), DFS (p = 0.035), and metastasis-free survival (MFS) (p = 0.038) in the ITF. Tumor-low status was associated with poorer prognosis. There was a significant correlation between the TSR and perineural invasion (PNI) in the inner tumor (p = 0.009).

CONCLUSIONS

The histopathological characteristics of different regions of OSCC may be used to develop the potential prognostic markers. The TSR of the inner tumor is more targeted in predicting prognosis and accurately assesses the risk of PNI+.

Tumor infiltrating lymphocytes and radiological picture of the tumor

Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.

Associations between dynamic-contrast enhanced MRI and tumor infiltrating lymphocytes and tumor-stroma ratio in head and neck squamous cell cancer

Objectives

The present study used dynamic-contrast enhanced MRI (DCE-MRI) to elucidate possible associations with tumor-infiltrating lymphocytes (TIL), stroma ratio and vimentin expression in head and neck squamous cell cancer (HNSCC).

Methods

Overall, 26 patients with primary HNSCC of different localizations were involved in the study. DCE-MRI was obtained on a 3 T MRI and analyzed with a whole lesion measurement using a histogram approach. TIL- and vimentin-expression was calculated on bioptic samples before any form of treatment. P16 staining was used to define HPV-status.

Results

Tumor-stroma ratio correlated with entropy derived from K^trans (r = − 0.52, p = 0.0071) and with kurtosis derived from V_e (r = − 0.53, p = 0.0058).

Several V_e derived parameters correlated with expression of TIL within the stroma compartment. TIL within the tumor compartment correlated with entropy derived from K^trans (r = 0.39, p = 0.047), p90 derived from V_e (r = 0.41, p = 0.036) and skewness derived from V_e (r = 0.41, p = 0.037). Furthermore, these associations were different between HPV positive and negative tumors.

Conclusions

DCE-MRI might be able to reflect tumor compartments and TIL expression in HNSCC. The most promising parameters were values derived from K^trans and V_e.