A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study

PURPOSE

Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).

MATERIALS AND METHODS

Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.

RESULTS

We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.

CONCLUSION

Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Exploring the effects of matrix metalloproteinase-13 on the malignant biological behavior of tongue squamous cell carcinoma via the TNF signaling pathway based on bioinformatics methods

Background

Identification of the etiology, molecular mechanisms, and carcinogenic pathways of tongue squamous cell carcinoma (TSCC) is crucial for developing new diagnostic and therapeutic strategies. This study used bioinformatics methods to identify key genes in TSCC and explored the potential functions and pathway mechanisms related to the malignant biological behavior of TSCC.

Methods

Gene chip data sets (i.e., GSE13601 and GSE34106) containing the data of both TSCC patients and normal control subjects were selected from the Gene Expression Omnibus (GEO) database. Using a gene expression analysis tool (GEO2R) of the GEO database, the differentially expressed genes (DEGs) were identified using the following criteria: |log fold change| >1, and P<0.05. The GEO2R tool was also used to select the upregulated DEGs in the chip candidates based on a P value <0.05. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) function analysis, and a protein-protein interaction (PPI) network analysis were then conducted. The results were displayed using R language packages, including volcano plots, Venn diagrams, heatmaps, and enriched pathway bubble charts. Genes from the MalaCards database were compared with the candidate genes, and a thorough review of the literature was conducted to determine the clinical significance of these genes. Finally, feature gene-directed chemical drugs or targeted drugs were predicted using the Comparative Toxicogenomics Database (CTD).

Results

In total, 767 upregulated DEGs were identified from GSE13601 and 695 from GSE34106. By intersecting the upregulated DEGs from both data sets using a Venn diagram, 100 DEGs related to TSCC were identified. The enrichment analysis of the KEGG signaling pathways identified the majority of the pathways associated with the upregulated DEGs, including the Toll-like receptor signaling pathway, the extracellular matrix-receptor interaction, the tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, the chemokine signaling pathway, the interlukin-17 signaling pathway, and natural killer cell-mediated cytotoxicity. The PPI network and module analyses of the shared DEGs ultimately resulted in five clusters and 55 candidate genes. A further intersection analysis of the TSCC-related genes in the MalaCards database via a Venn diagram identified three important shared DEGs; that is, matrix metalloproteinase-1 (MMP1), MMP9, and MMP13. In the CTD, seven drugs related to MMP13 were identified for treating tongue tumors.

Conclusions

This study identified key genes and signaling pathways involved in TSCC and thus extended understandings of the molecular mechanisms that underlie the development and progression of TSCC. Additionally, this study showed that MMP13 may influence the malignant biological behavior of TSCC through the TNF signaling pathway. This finding could provide a theoretical basis for research into early differential diagnosis and targeted treatment.

A literature review: machine learning-based stem cell investigation

Background and Objective

Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement.

Methods

In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions.

Key Content and Findings

The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn't consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions.

Conclusions

The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

LncRNA CCAT1 knockdown suppresses tongue squamous cell carcinoma progression by inhibiting the ubiquitination of PHLPP2

Tongue squamous cell carcinoma (TSCC) is prevailing malignancy in the oral and maxillofacial region, characterized by its high frequency. LncRNA CCAT1 can promote tumorigenesis and progression in many cancers. Here, we investigated the regulatory mechanism by which CCAT1 influences growth and metastasis of TSCC. Levels of CCAT1, WTAP, TRIM46, PHLPP2, AKT, p-AKT, and Ki67 in TSCC tissues and cells were assessed utilizing qRT-PCR, Western blot and IHC. Cell proliferation, migration, and invasion were evaluated utilizing CCK8, colony formation, wound healing and transwell assays. Subcellular localization of CCAT1 was detected utilizing FISH assay. m6A level of CCAT1 was assessed using MeRIP. RNA immunoprecipitation (RIP), Co-immunoprecipitation (Co-IP) and RNA pull down elucidated binding relationship between molecules. Nude mouse tumorigenesis experiments were used to verify the TSCC regulatory function of CCAT1 in vivo. Metastatic pulmonary nodules were observed utilizing hematoxylin and eosin (HE) staining. CCAT1 silencing repressed TSCC cell proliferation, migration and invasion. Expression of CCAT1 was enhanced through N6-methyladenosine (m6A) modification of its RNA, facilitated by WTAP. Moreover, IGF2BP1 up-regulated CCAT1 expression by stabilizing its RNA transcript. CCAT1 bond to PHLPP2, inducing its ubiquitination and activating AKT signaling. CCAT1 mediated the ubiquitination and degradation of PHLPP2 by TRIM46, thereby promoting TSCC growth and metastasis. CCAT1/TRIM46/PHLPP2 axis regulated proliferation and invasion of TSCC cells, implying that CCAT1 would be a novel therapeutic target for TSCC patients.

[Expression of interleukin-34 in tongue squamous cell carcinoma and its clinical implications]

OBJECTIVE

To investigate the expression of interleukin- 34 (IL-34) in tongue squamous cell carcinoma (TSCC) and its clinical implications.

METHODS

Serum IL-34 level was detected in 36 patients with TSCC and 36 healthy individuals using enzymelinked immunosorbent assay (ELISA). The expressions of IL-34 mRNA and protein levels in TSCC and adjacent tissues were examined in 41 patients using real-time fluorescence quantitative PCR (qRT-PCR) and immunohistochemistry (IHC), and their correlation with the clinicopathological features of the patients was further analyzed. Informatic analysis of the differentially expressed genes related with IL-34 in TSCC was carried out based on String database, LinkedOmics database and GEO database, and GO functional analysis and KEGG signaling pathway enrichment analysis were performed using Webgestalt database.

RESULTS

The serum level of IL-34 was significantly lower in TSCC patients than in the healthy individuals (P < 0.001), and its expression level was also significantly lower in the tumor tissues than in the adjacent tissues (P < 0.001). The expression level of IL-34 in TSCC tissues was related with lymph node metastasis and TNM staging (P < 0.05), but not with age, gender, smoking, drinking, or tumor size (P > 0.05). Informatic analysis suggested that IL-34 had the strongest correlation with CSF1R and PTPRJ. IL-34 and its related genes in TSCC were enriched mainly in bone marrow cell differentiation, collagen-containing extracellular matrix, and cytokine binding and signal receptor activator activity. KEGG signaling pathway enrichment showed that IL-34 and the related differentially expressed genes were involved mainly in osteoclast differentiation, protein polysaccharide in cancer, and the MAPK signaling pathway.

CONCLUSION

IL-34 is lowly expressed in TSCC and participates in the occurrence and progression of TSCC, and can be potentially used as a new diagnostic biomarker and therapeutic target for TSCC.

A new prognostic model of esophageal squamous cell carcinoma based on Cloud-least squares support vector machine

Background

In view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC.

Methods

We studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation.

Results

The overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM.

Conclusions

Due to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models.

Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

BACKGROUND

The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors.

METHODS

A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used.

RESULTS

Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal-Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features.

CONCLUSIONS

Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling

Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis (P < .05). In the test set, the combined model based on these factors had the highest predictive performance for TSCC prognosis (area under curve (AUC) AUC: 0.870, 95% CI [0.761-0.942]), which was significantly higher than the clinical model (AUC: 0.730, 95% CI [0.602-0.835], P = .033) and imaging model (AUC: 0.765, 95% CI [0.640-0.863], P = .074). The decision curve also confirmed the higher clinical net benefit of the combined model, and these results were validated in the test set. The nomogram developed based on the combined model received good evaluation in clinical application. Conclusion: MR-LASSO extracted texture parameters can help improve the performance of TSCC prognosis models. The combined model and nomogram provide support for postoperative clinical treatment management of TSCC.