CT-based radiomics features in the prediction of thyroid cartilage invasion from laryngeal and hypopharyngeal squamous cell carcinoma

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature

OBJECTIVES

To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis.

RESULTS

Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC.

CLINICAL RELEVANCE STATEMENT

Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making.

KEY POINTS

• Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects

BACKGROUND

Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction.

OBJECTIVES

This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges.

METHODS

Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability.

CONCLUSION

Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling

Objective

Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival.

Materials and methods

We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software.

Results

The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (>670 mm3) of infiltration had a worse prognosis compared to those with a lower volume.

Conclusions

Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor.

Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study

OBJECTIVE

Advanced laryngeal cancers are clinically complex; there is a paucity of modern decision-making models to guide tumour-specific management. This pilot study aims to identify computed tomography-based radiomic features that may predict survival and enhance prognostication.

METHODS

Pre-biopsy, contrast-enhanced computed tomography scans were assembled from a retrospective cohort (n = 72) with advanced laryngeal cancers (T3 and T4). The LIFEx software was used for radiomic feature extraction. Two features: shape compacity (irregularity of tumour volume) and grey-level zone length matrix - grey-level non-uniformity (tumour heterogeneity) were selected via least absolute shrinkage and selection operator-based Cox regression and explored for prognostic potential.

RESULTS

A greater shape compacity (hazard ratio 2.89) and grey-level zone length matrix - grey-level non-uniformity (hazard ratio 1.64) were significantly associated with worse 5-year disease-specific survival (p < 0.05). Cox regression models yielded a superior C-index when incorporating radiomic features (0.759) versus clinicopathological variables alone (0.655).

CONCLUSIONS

Two radiomic features were identified as independent prognostic biomarkers. A multi-centre prospective study is necessary for further exploration. Integrated radiomic models may refine the treatment of advanced laryngeal cancers.

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma

RATIONALE AND OBJECTIVES

Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) based on the venous-phase CT images and explored the performance of these models in stratifying patients with laryngeal carcinoma into stage I-II and stage III-IV, and also compared these models with radiologists.

MATERIALS AND METHODS

Three hundreds and nineteen patients with pathologically confirmed laryngeal carcinoma were randomly divided into a training set (n = 223) and a test set (n = 96). In the training set, the radiomics features with inter- and intraclass correlation coefficients (ICCs)> 0.75 were screened by Spearman correlation analysis and recursive feature elimination (RFE); then support vector machine (SVM) classifier was applied to develop the radiomics model. The DL model was built using ResNet 18 by the cropped 2D regions of interest (ROIs) in the maximum tumor ROI slices and the last fully connected layer of this network served as the DL feature extractor. Finally, a combined model was developed by pooling the radiomics features and extracted DL features to predict the staging.

RESULTS

The area under the curves (AUCs) for radiomics model, DL model, and combined model in the test set were 0.704 (95% confidence interval [CI]: 0.588-0.820), 0.724 (95% CI: 0.613-0.835), and 0.849 (95% CI: 0.755-0.943), respectively. The combined model outperformed the radiomics model and the DL model in discriminating stage I-II from stage III-IV (p = 0.031 and p = 0.020, respectively). Only the combined model performed significantly better than radiologists (p < 0.050 for both).

CONCLUSION

The combined model can help tailor the therapeutic strategy for laryngeal carcinoma patients by enabling more accurate preoperative staging.

Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review

OBJECTIVE

To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.

DATA SOURCES

Web of Science, Embase, PubMed, and Cochrane Library.

REVIEW METHODS

Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.

RESULTS

Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.

CONCLUSION

This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.

Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center

Accurate classification of laryngeal cancer is a critical step for diagnosis and appropriate treatment. Radiomics is a rapidly advancing field in medical image processing that uses various algorithms to extract many quantitative features from radiological images. The high dimensional features extracted tend to cause overfitting and increase the complexity of the classification model. Thereby, feature selection plays an integral part in selecting relevant features for the classification problem. In this study, we explore the predictive capabilities of radiomics on Computed Tomography (CT) images with the incidence of laryngeal cancer to predict the histopathological grade and T stage of the tumour. Working with a pilot dataset of 20 images, an experienced radiologist carefully annotated the supraglottic lesions in the three-dimensional plane. Over 280 radiomic features that quantify the shape, intensity and texture were extracted from each image. Machine learning classifiers were built and tested to predict the stage and grade of the malignant tumour based on the calculated radiomic features. To investigate if radiomic features extracted from CT images can be used for the classification of laryngeal tumours. Out of 280 features extracted from every image in the dataset, it was found that 24 features are potential classifiers of laryngeal tumour stage and 12 radiomic features are good classifiers of histopathological grade of the laryngeal tumor. The novelty of this paper lies in the ability to create these classifiers before the surgical biopsy procedure, giving the clinician valuable, timely information.

Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review

(1) Background: Hypopharyngeal squamous cell carcinomas usually present with locally advanced disease and a correspondingly poor prognosis. Currently, efforts are being made to improve tumor characterization and provide insightful information for outcome prediction. Radiomics is an emerging area of study that involves the conversion of medical images into mineable data; these data are then used to extract quantitative features based on shape, intensity, texture, and other parameters; (2) Methods: A systematic review of the peer-reviewed literature was conducted; (3) Results: A total of 437 manuscripts were identified. Fifteen manuscripts met the inclusion criteria. The main targets described were the evaluation of textural features to determine tumor-programmed death-ligand 1 expression; a surrogate for microvessel density and heterogeneity of perfusion; patient stratification into groups at high and low risk of progression; prediction of early recurrence, 1-year locoregional failure and survival outcome, including progression-free survival and overall survival, in patients with locally advanced HPSCC; thyroid cartilage invasion, early disease progression, recurrence, induction chemotherapy response, treatment response, and prognosis; and (4) Conclusions: our findings suggest that radiomics represents a potentially useful tool in the diagnostic workup as well as during the treatment and follow-up of patients with HPSCC. Large prospective studies are essential to validate this technology in these patients.

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

OBJECTIVE

This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC).

DATA SOURCES

Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE.

REVIEW METHODS

A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness.

CONCLUSIONS

AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality.

IMPLICATIONS FOR PRACTICE

AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., "COVID-19", "Pneumonia", and "Normal". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.

Automated segmentation of the larynx on computed tomography images: a review

The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement. This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work. We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-022-00221-3.

[Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model]

Radiomics, a technique for quantitative analysis of tumor imaging information through high-throughput extraction, uses a non-invasive way to capture a large number of internal heterogeneity characteristics of tumors, providing imaging basis for tumor staging and typing, tumor invasion site and distant metastasis, postoperative induction chemotherapy and prognosis, and providing new ideas and new thinking for the field of personalized precision medicine of tumors. This review aims to briefly summarize the latest research progress of imaging omics in the diagnosis and treatment design of head and neck tumor, and to discuss the research progress of constructing the treatment plan and prognosis evaluation model of hypopharyngeal cancer based on imaging omics, and to predict and forecast its development direction and clinical application.

Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study

PURPOSE

To develop and validate a dual-energy CT (DECT)-based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC).

METHODS

A total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed.

RESULTS

The tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree.

CONCLUSION

The nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation.

Artificial Intelligence and Deep Learning of Head and Neck Cancer

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.

Radiomics in Oncology: A 10-Year Bibliometric Analysis

Objectives

To date, radiomics has been applied in oncology for over a decade and has shown great progress. We used a bibliometric analysis to analyze the publications of radiomics in oncology to clearly illustrate the current situation and future trends and encourage more researchers to participate in radiomics research in oncology.

Methods

Publications for radiomics in oncology were downloaded from the Web of Science Core Collection (WoSCC). WoSCC data were collected, and CiteSpace was used for a bibliometric analysis of countries, institutions, journals, authors, keywords, and references pertaining to this field. The state of research and areas of focus were analyzed through burst detection.

Results

A total of 7,199 pieces of literature concerning radiomics in oncology were analyzed on CiteSpace. The number of publications has undergone rapid growth and continues to increase. The USA and Chinese Academy of Sciences are found to be the most prolific country and institution, respectively. In terms of journals and co-cited journals, Scientific Reports is ranked highest with respect to the number of publications, and Radiology is ranked highest among co-cited journals. Moreover, Jie Tian has published the most publications, and Phillipe Lambin is the most cited author. A paper published by Gillies et al. presents the highest citation counts. Artificial intelligence (AI), segmentation methods, and the use of radiomics for classification and diagnosis in oncology are major areas of focus in this field. Test-retest statistics, including reproducibility and statistical methods of radiomics research, the relation between genomics and radiomics, and applications of radiomics to sarcoma and intensity-modulated radiotherapy, are frontier areas of this field.

Conclusion

To our knowledge, this is the first study to provide an overview of the literature related to radiomics in oncology and may inspire researchers from multiple disciplines to engage in radiomics-related research.

The application of radiomics in laryngeal cancer

OBJECTIVES

Radiomics is the conversion of medical images into quantitative high-dimensional data. Laryngeal cancer, one of the most common head and neck cancers, has risen globally by 58.7%. CT, MRI and PET are acquired during the diagnostic process providing potential data for radiomic analysis and correlation with outcomes.This review aims to examine the applications of this technique to laryngeal cancer and the future considerations for translation into clinical practice.

METHODS

A comprehensive systematic review-informed search of the MEDLINE and EMBASE databases was undertaken. Keywords "laryngeal cancer" OR "larynx" OR "larynx cancer" OR "head and neck cancer" were combined with "radiomic" OR "signature" OR "machine learning" OR "artificial intelligence". Additional articles were obtained from bibliographies using the "snowball method".

RESULTS

The included studies (n = 15) demonstrated that radiomic features are significantly associated with various clinical outcomes (including stage, overall survival, treatment response, progression-free survival) and that predictive models incorporating radiomic features are superior to those that do not. Two studies demonstrated radiomics could improve laryngeal cancer staging whilst 12 studies affirmed its predictive capability for clinical outcomes.

CONCLUSIONS

Radiomics has potential for improving multiple aspects of laryngeal cancer care; however, the heterogeneous cohorts and lack of data on laryngeal cancer exclusively inhibits firm conclusions. Large prospective well-designed studies in laryngeal cancer are required to progress this field. Furthermore, to implement radiomics into clinical practice, a unified research effort is required to standardise radiomics practice.

ADVANCES IN KNOWLEDGE

This review has highlighted the value of radiomics in enhancing laryngeal cancer care (including staging, prognosis and predicting treatment response).

Artificial Intelligence in Thyroid Field-A Comprehensive Review

Simple Summary

The incidence of thyroid pathologies has been increasing worldwide. Historically, the detection of thyroid neoplasms relies on medical imaging analysis, depending mainly on the experience of clinicians. The advent of artificial intelligence (AI) techniques led to a remarkable progress in image-recognition tasks. AI represents a powerful tool that may facilitate understanding of thyroid pathologies, but actually, the diagnostic accuracy is uncertain. This article aims to provide an overview of the basic aspects, limitations and open issues of the AI methods applied to thyroid images. Medical experts should be familiar with the workflow of AI techniques in order to avoid misleading outcomes.

Abstract

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Cancer of the Larynx and Hypopharynx

The Radiation Therapy Oncology Group 91-11 trial and US Veterans Affairs trial revolutionized the way locally advanced laryngeal cancers are treated. Adjuvant therapies exist aimed toward laryngeal preservation using docetaxel, cisplatin, and fluorouracil. Cetuximab is a cornerstone of treatment due to the large role of epidermal growth factor receptor in laryngeal and hypopharyngeal carcinomas. In addition, the immune system is vital in the prevention of recurrence, and various immunomodulators against programmed cell death receptor 1 are being investigated. Multidisciplinary management of the patient with laryngeal and hypopharyngeal is key, as many vital functions are affected by this devastating disease.

A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images

BACKGROUND

Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard.

PATIENT AND METHODS

Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan-Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI).

RESULTS

A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation).

CONCLUSION

A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.