Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status

Computer Vision-Radiomics & Pathognomics

The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.

Prognosis Prediction in Head and Neck Squamous Cell Carcinoma by Radiomics and Clinical Information

(1) Background: head and neck squamous cell carcinoma (HNSCC) is a common cancer whose prognosis is affected by its heterogeneous nature. We aim to predict 5-year overall survival in HNSCC radiotherapy (RT) patients by integrating radiomic and clinical information in machine-learning models; (2) Methods: HNSCC radiotherapy planning computed tomography (CT) images with RT structures were obtained from The Cancer Imaging Archive. Radiomic features and clinical data were independently analyzed by five machine-learning algorithms. The results were enhanced through a voted ensembled approach. Subsequently, a probability-weighted enhanced model (PWEM) was generated by incorporating both models; (3) Results: a total of 299 cases were included in the analysis. By receiver operating characteristic (ROC) curve analysis, PWEM achieved an area under the curve (AUC) of 0.86, which outperformed both radiomic and clinical factor models. Mean decrease accuracy, mean decrease Gini, and a chi-square test identified T stage, age, and disease site as the most important clinical factors in prognosis prediction; (4) Conclusions: our radiomic-clinical combined model revealed superior performance when compared to radiomic and clinical factor models alone. Further prospective research with a larger sample size is warranted to implement the model for clinical use.

A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer

Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.

Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy

BACKGROUND AND PURPOSE

We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC).

MATERIALS AND METHODS

A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated.

RESULTS

The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia.

CONCLUSION

Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

BACKGROUND

We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC.

METHOD

We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort.

RESULTS

A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset.

CONCLUSIONS AND RELEVANCE

We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status

Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.

MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

BACKGROUND

. At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset.

METHODS

For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108).

RESULTS

The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage.

CONCLUSIONS

Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.

CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma

Chemoradiation is a common therapeutic regimen for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). However, not all patients benefit from chemotherapy, especially patients with low-risk characteristics. We aim to develop and validate a prognostic and predictive radiomic image signature (pRiS) to inform survival and chemotherapy benefit using computed tomography (CT) scans from 491 stage I and II HPV-associated OPSCC, which were divided into three cohorts D₁-D₃. The prognostic performance of pRiS was evaluated on two test sets (D₂, n = 162; D₃, n = 269) using concordance index. Patients from D₂ and D₃ who received either radiotherapy alone or chemoradiation were used to validate pRiS as predictive of added benefit of chemotherapy. Seven features were selected to construct pRiS, which was found to be prognostic of overall survival (OS) on univariate analysis in D₂ (hazard ratio [HR] = 2.14, 95% confidence interval [CI], 1.1-4.16, p = 0.02) and D₃ (HR = 2.74, 95% CI, 1.34-5.62, p = 0.006). Chemotherapy was associated with improved OS for high-pRiS patients in D₂ (radiation vs chemoradiation, HR = 4.47, 95% CI, 1.73-11.6, p = 0.002) and D₃ (radiation vs chemoradiation, HR = 2.99, 95% CI, 1.04-8.63, p = 0.04). In contrast, chemotherapy did not improve OS for low-pRiS patients, which indicates these patients did not derive additional benefit from chemotherapy and could be considered for treatment de-escalation. The proposed radiomic signature was prognostic of patient survival and informed benefit from chemotherapy for stage I and II HPV-associated OPSCC patients.

External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer

PURPOSE/OBJECTIVE

The purpose of the study is to externally validate published ¹⁸F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy.

MATERIAL/METHODS

Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (F_i), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score R_F derived from F_i was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score R_F were also tested on the entire dataset.

RESULTS

Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. R_F and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (R_F>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group.

CONCLUSION

A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.

Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma

OBJECTIVES

Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics.

MATERIALS AND METHODS

Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis.

RESULTS

Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72).

CONCLUSION

Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics.

Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?

This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (p-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank p-value of 3.30 × 10⁻⁵ and C-index of 0.80, compared with log-rank p-values from 3.52 × 10⁻² to 1.15 × 10⁻⁴ and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer.

Events prediction after treatment in HPV-driven oropharyngeal carcinoma using machine learning

OBJECTIVES

Our objective was to develop a predictive model using a machine learning signature to identify patients at high risk of relapse or death after treatment for HPV-positive oropharyngeal carcinoma.

MATERIALS AND METHODS

Pre-treatment variables of 450 patients with HPV-positive oropharyngeal carcinoma treated with a curative intent comprised clinical items, imaging parameters and histological findings. The events considered were progression or residual disease after treatment, the recurrent disease after a disease-free interval and death. The endpoints were the prediction of events and progression-free survival. After feature Z-score normalisation and selection, random forest classifier models were trained. The best models were evaluated on recall, the F-score, and the ROC AUC metric. The clinical relevance of the best prediction model was evaluated using Kaplan-Meier analysis with a log-rank test.

RESULTS

The best random forest model predicted the 5-year risk of relapse-free survival with a recall of 79.1%, an F1-score of 81.08%, and an AUC of the ROC curve of 0.89. The models performed poorly for the prediction of specific events of progression only, recurrence only or death only. The clinical relevance of the model was validated with a 5-year relapse-free survival of high-risk patients versus low-risk patients of 23.5% and 80%, respectively (p < 0.0001).

CONCLUSION

Patients with HPV-driven oropharyngeal carcinoma at high risk of relapse-free survival could be identified with a predictive machine learning model using patient data before treatment.

Preoperative Survival Prediction in Intrahepatic Cholangiocarcinoma Using an Ultrasound-Based Radiographic-Radiomics Signature

OBJECTIVES

To construct a preoperative model for survival prediction in intrahepatic cholangiocarcinoma (ICC) patients using ultrasound (US) based radiographic-radiomics signatures.

METHODS

Between April 2010 and September 2015, 170 patients with ICC who underwent curative resection were retrospectively recruited. Overall survival (OS)-related radiographic signatures and radiomics signatures based on preoperative US were built and assessed through a time-dependent receiver operating characteristic curve analysis. A nomogram was developed based on the selected predictors from the radiographic-radiomics signatures and clinical and laboratory results of the training cohort (n = 127), validated in an independent testing cohort (n = 43) by the concordance index (C-index), and compared with the Tumor Node Metastasis (TNM) cancer staging system as well as the radiographic and radiomics nomograms.

RESULTS

The median areas under the curve of the radiomics signature and radiographic signature were higher than that of the TNM staging system in the testing cohort, although the values were not significantly different (0.76-0.82 versus 0.62, P = .485 and .264). The preoperative nomogram with CA 19-9, sex, ascites, radiomics signature, and radiographic signature had C-indexes of 0.72 and 0.75 in the training and testing cohorts, respectively, and it had significantly higher predictive performance than the 8th TNM staging system in the testing cohort (C-index: 0.75 versus 0.67, P = .004) and a higher C-index than the radiomics nomograms (0.75 versus 0.68, P = .044).

CONCLUSIONS

The preoperative nomogram integrated with the radiographic-radiomics signature demonstrated good predictive performance for OS in ICC and was superior to the 8th TNM staging system.

Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma

Objective

We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).

Methods

We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.

Results

After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.

Conclusion

We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.

Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study

Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer (HNC), a group of malignancies associated with high heterogeneity. However, the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck (HN) tissues. In particular, feature repeatability of radiomics in magnetic resonance imaging (MRI) acquisition, which is considered a crucial confounding factor of radiomics feature reliability, is still sparsely investigated. This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers, aiming for potential magnetic resonance-guided radiotherapy (MRgRT) treatment of HNC. Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5 T MRI simulator. The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient (ICC), whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation (CV). The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types, tissues, and pulse sequences. Only a small fraction of features showed excellent acquisition repeatability (ICC > 0.9) and low within-subject variability. Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans. This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.

Salivary Metabolomics for Prognosis of Oral Squamous Cell Carcinoma

This study aimed to identify salivary metabolomic biomarkers for predicting the prognosis of oral squamous cell carcinoma (OSCC) based on comprehensive metabolomic analyses. Quantified metabolomics data of unstimulated saliva samples collected from patients with OSCC (n = 72) were randomly divided into the training (n = 35) and validation groups (n = 37). The training data were used to develop a Cox proportional hazards regression model for identifying significant metabolites as prognostic factors for overall survival (OS) and disease-free survival. Moreover, the validation group was used to develop another Cox proportional hazards regression model using the previously identified metabolites. There were no significant between-group differences in the participants’ characteristics, including age, sex, and the median follow-up periods (55 months [range: 3–100] vs. 43 months [range: 0–97]). The concentrations of 5-hydroxylysine (p = 0.009) and 3-methylhistidine (p = 0.012) were identified as significant prognostic factors for OS in the training group. Among them, the concentration of 3-methylhistidine was a significant prognostic factor for OS in the validation group (p = 0.048). Our findings revealed that salivary 3-methylhistidine is a prognostic factor for OS in patients with OSCC.

Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas

Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.

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).

Reliability of MRI radiomics features in MR-guided radiotherapy for prostate cancer: Repeatability, reproducibility, and within-subject agreement

PURPOSE

The MR-guided radiotherapy (MRgRT) images on the integrated MRI and linear accelerator (MR-LINAC) might facilitate radiomics analysis for longitudinal treatment response assessment. However, the reliability of MRgRT radiomics features is largely unknown. This study aims to investigate MRgRT radiomics feature reliability acquired using a standardized 3D-T2W-TSE sequence in terms of repeatability, reproducibility, and within-subject feature agreement on a 1.5T MR-simulator and a 1.5T MR-LINAC for prostate cancer (PC).

METHODS

Twenty-six consecutive PC patients who underwent one MRI-simulator scan and two MR-LINAC scans before dose delivery were retrospectively included. The three MRI datasets were rigidly co-registered. 1023 first-order and texture radiomics features were extracted with different intensity bin widths for each scan in the manually segmented clinical target volume (CTV) and planning target volume (PTV) by an experienced radiation oncologist. Intraclass correlation coefficient (ICC) was used to evaluate feature repeatability between MR-LINAC scans and reproducibility between MRI-simulator and MR-LINAC scans. The within-subject feature value agreements were evaluated using Bland-Altman analysis. The impact of inter-observer segmentation on the radiomics feature reliability was also examined based on the second manual segmentation of CTV and PTV by an MRI researcher.

RESULTS

Based on the segmentation by the radiation oncologist and the default bin width of 25, 9.6%, 24.1%, 49.6%, and 16.8% of the total 1023 features exhibited excellent (ICC > 0.9), good (0.9 > ICC > 0.75), moderate (0.75 > ICC > 0.5), and poor (ICC < 0.5) repeatability in the CTV, and 9.2%, 26.8%, 50.5%, and 13.5% in the PTV, respectively. For reproducibility, the corresponding feature percentages were 8.9%, 19.7%, 41.9%, and 29.6% in the CTV, and 8.4%, 17.8%, 47.9%, and 26% in the PTV. Feature reliability was not notably influenced by intensity bin width for discretization. BA analysis revealed wide 95% limit-of-agreements and substantial biases of feature values between CTV and PTV and between any two MRI scans. The features even with excellent ICC were still subjected to considerable inter-scan feature variations in each individual subject. The analysis on the second segmentation by the MRI researcher showed insignificantly different feature repeatability and reproducibility in terms of ICC values.

CONCLUSIONS

Only a small proportion of features exhibited excellent/good repeatability and reproducibility, highlighting the importance of reliable MRgRT feature selection. The within-subject feature values were subjected to considerable inter-scan variations, imposing a challenge on the determination of the smallest detectable change in future MRgRT delta-radiomics studies.

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models

Simple Summary

Head and neck squamous cell carcinoma (HNSCC) is the most common mucosal malignancy of the head and neck and a leading cause of cancer death. HNSCC arises from different primary anatomical locations that are typically combined during radiomic analyses assuming that the radiomic features, i.e., quantitative image-based features, are similar based on histopathologic characteristics. However, whether these quantitative features are comparable across tumor sites remains unknown. The aim of our retrospective study was to assess if systematic differences exist between radiomic features based on different tumor sites in HNSCC and how they might affect machine learning model performance in endpoint prediction. Using a population of 605 HNSCC patients, we observed significant differences in radiomic features of tumors from different locations and showed that these differences can impact machine learning model performance. This suggests that tumor site should be considered when developing and evaluating radiomics-based models.

Abstract

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.

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.

Machine learning applications in radiation oncology

Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers

PURPOSE

This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence-free survival (RFS).

MATERIALS AND METHODS

Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational ¹⁸ F-FDG-PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume.

RESULTS

While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ 2 test) and RFS (HR = 7.53, 95% CI = 2.54-22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26-5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03-7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches.

CONCLUSIONS

Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.

Targeting Treatment Resistance in Head and Neck Squamous Cell Carcinoma - Proof of Concept for CT Radiomics-Based Identification of Resistant Sub-Volumes

Purpose

Radiomics has already been proposed as a prognostic biomarker in head and neck cancer (HNSCC). However, its predictive power in radiotherapy has not yet been studied. Here, we investigated a local radiomics approach to distinguish between tumor sub-volumes with different levels of radiosensitivity as a possible target for radiation dose intensification.

Materials and Methods

Of 40 patients (n=28 training and n=12 validation) with biopsy confirmed locally recurrent HNSCC, pretreatment contrast-enhanced CT images were registered with follow-up PET/CT imaging allowing identification of controlled (GTVcontrol) vs non-controlled (GTVrec) tumor sub-volumes on pretreatment imaging. A bi-regional model was built using radiomic features extracted from pretreatment CT in the GTVrec and GTVcontrol to differentiate between those regions. Additionally, concept of local radiomics was implemented to perform detection task. The original tumor volume was divided into sub-volumes with no prior information on the location of recurrence. Radiomic features from those sub-volumes were then used to detect recurrent sub-volumes using multivariable logistic regression.

Results

Radiomic features extracted from non-controlled regions differed significantly from those in controlled regions (training AUC = 0.79 CI 95% 0.66 - 0.91 and validation AUC = 0.88 CI 95% 0.72 – 1.00). Local radiomics analysis allowed efficient detection of non-controlled sub-volumes both in the training AUC = 0.66 (CI 95% 0.56 – 0.75) and validation cohort 0.70 (CI 95% 0.53 – 0.86), however performance of this model was inferior to bi-regional model. Both models indicated that sub-volumes characterized by higher heterogeneity were linked to tumor recurrence.

Conclusion

Local radiomics is able to detect sub-volumes with decreased radiosensitivity, associated with location of tumor recurrence in HNSCC in the pre-treatment CT imaging. This proof of concept study, indicates that local CT radiomics can be used as predictive biomarker in radiotherapy and potential target for dose intensification.

Deciphering the glioblastoma phenotype by computed tomography radiomics

INTRODUCTION

Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM.

MATERIALS AND METHODS

Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated.

RESULTS

Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71.

CONCLUSION

In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Background

MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT).

Methods

An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations.

Results

The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively.

Conclusions

This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.

Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics

Background

The purpose of this study is to determine if Haralick texture analysis on CT imaging of mucoepidermoid carcinomas (MEC) can differentiate low-grade and high-grade tumors.

Methods

A retrospective review of 18 patients with MEC of the salivary glands, corresponding CT imaging and pathology report was performed. Tumors were manually segmented and image analysis was performed to calculate radiomic features. Radiomic features were compared between low-grade and high-grade MEC. A multivariable logistic regression model and receiver operating characteristic analysis was performed.

Results

A total of 18 patients (mean age, 51, range 9-83 years, 8 men and 10 women) were included. Nine patients had low-grade pathology and nine patients had high-grade pathology. Of the 18 cases, 7 (39%) occurred in the parotid gland and 11 (61%) occurred in minor salivary glands. No individual feature was significantly different between low-grade and high-grade MEC. A logistic regression model including surface regularity, energy and information measure II of correlation was performed and was able to predict high-grade MEC accurately (sensitivity 89%, specificity 68%). The area under the receiver operating characteristic curve was 0.802.

Conclusions

High-grade MEC tend to have a low energy, high correlation texture as well as surface irregularity. Together, these three features may comprise a tumor phenotype that is able to predict high-grade pathology in MECs.

Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence

PURPOSE

The purpose of this study was to quantitatively assess the longitudinal acquisition repeatability of MRI radiomics features in a three-dimensional (3D) T1-weighted (T1W) TSE sequence via a well-controlled prospective phantom study.

METHODS

Thirty consecutive daily datasets of an ACR-MRI phantom were acquired on two 1.5T MRI simulators using a 3D T1W TSE sequence. Images were blindly segmented by two observers. Post-acquisition processing was minimized but an intensity discretization (fixed bin size of 25). One hundred and one radiomics features (shape n = 12; first order n = 16; texture n = 73) were extracted. Longitudinal repeatability of each feature was evaluated by Pearson correlation and coefficient of variance (CV_68% ). Interobserver feature value agreement was also quantified using intraclass correlation coefficient (ICC) and Bland-Altman analysis. A most repeatable radiomics feature set on both scanners was determined by feature coefficient of variance (CV_68% <5%), ICC (>0.75), and the ratio of the interobserver difference to the interobserver mean δ<5%.

RESULTS

No trend of radiomics feature value changed with time. Longitudinal feature repeatability CV_68% ranged 0.01-38.60% (mean/median: 12.5%/9.9%), and 0.01-40.47%, (8.49%/7.34%) on the scanners A and B. Shape features exhibited significantly better repeatability than first-order and texture features (all P < 0.01). Significant longitudinal repeatability difference was observed in texture features (P < 0.001) between the two scanners, but not in shape and first-order features (P > 0.30). First-order and texture features had smaller interobserver-dependent variation than acquisition-dependent variation. They also showed good interobserver agreement on both scanners (A:ICC = 0.80 ± 0.23; B:ICC = 0.80 ± 0.22), independent of acquisition repeatability. The repeatable radiomics features in common on both scanners, including 12 shape features, 0 first-order features, and 3 texture features, were determined as the most repeatable MRI radiomics feature set.

CONCLUSIONS

Radiomics features exhibited heterogeneous longitudinal repeatability, while the shape features were the most repeatable, in this phantom study with a 3D T1W TSE acquisition. The most repeatable radiomics feature set derived in this study should be helpful for the selection of reliable radiomics features in the future clinical use.

Reliability of radiomics features due to image reconstruction using a standardized T2 -weighted pulse sequence for MR-guided radiotherapy: An anthropomorphic phantom study

PURPOSE

To prospectively investigate the impact of image reconstruction on MRI radiomics features.

METHODS

An anthropomorphic phantom was scanned at 1.5 T using a standardized sequence for MR-guided radiotherapy under SENSE and compressed-SENSE reconstruction settings. A total of 93 first-order and texture radiomics features in 10 volumes of interest were assessed based on (1) accuracy measured by the percentage deviation from the reference, (2) robustness on reconstruction in all volumes of interest measured by the intraclass correlation coefficient, and (3) repeatability measured by the coefficient of variance over the repetitive acquisitions. Finally, reliable and unreliable radiomics features were comprehensively determined based on their accuracy, robustness, and repeatability.

RESULTS

Better accuracy and robustness of the radiomics features were achieved under SENSE than compressed-SENSE reconstruction. The feature accuracy under SENSE reconstruction was more affected by acceleration factor than direction, whereas under compressed-SENSE reconstruction, accuracy was substantially impacted by the increasing denoising levels. Feature repeatability was dependent more on feature types than on reconstruction. A total of 45 reliable features and 13 unreliable features were finally determined for SENSE, compared with 22 reliable and 26 unreliable features for compressed SENSE. First-order and gray-level co-occurrence matrix features were generally more reliable than other features.

CONCLUSION

Radiomics features could be substantially affected by MRI reconstruction, so precautions need to be taken regarding their reliability for clinical use. This study helps the guidance of the preselection of reliable radiomics features and the preclusion of unreliable features in MR-guided radiotherapy.

Application of radiomics and machine learning in head and neck cancers

With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.

Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers

BACKGROUND

There is no evidence to support surgery or radiotherapy as the best treatment for resectable oropharyngeal cancers with a negative HPV status. Predictive algorithms may help to decide which strategy to choose, but they will only be accepted by caregivers and European authorities if they are interpretable. As a proof of concept, we developed a predictive and interpretable algorithm to predict locoregional relapse at 18 months for oropharyngeal cancers as a first step towards that goal.

METHODS

The model was based on clinical and Pyradiomics features extracted from the dosimetric CT scan. Intraclass correlation was used to filter out features dependant on delineation. Correlated redundant features were also removed. An XGBoost model was cross-validated and optimised on the HN1 cohort (79 patients), and performances were assessed on the ART ORL cohort (45 patients). The Shapley Values were used to provide an overall and local explanation of the model.

RESULTS

On the ART ORL cohort, the model trained on HN1 yielded a precision-or predictive positive value-of 0.92, a recall of 0.42, an area under the curve of the receiver operating characteristic of 0.68 and an accuracy of 0.64. The most contributory features were shape Voxel Volume, grey level size zone matrix Small Area Emphasis (glszmSAE), gldm Dependence Non Uniformity Normalized (gldmDNUN), Sex and Age.

CONCLUSIONS

We developed an interpretable and generalizable model that could yield a good precision-positive predictive value-for relapse at 18 months on a different test cohort.

Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients

This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10-2, 1.85 [Formula: see text] 10-2, 3.17 [Formula: see text] 10-2, and 1.87 [Formula: see text] 10-3, respectively. When the features were integrated with clinical variables, the p values of conventional, index, and combined features were 3.53 [Formula: see text] 10-3, 1.28 [Formula: see text] 10-3, and 1.45 [Formula: see text] 10-3, respectively. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients.

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Introduction: Prognosis prediction is essential to improve therapeutic strategies and to achieve better clinical outcomes in colorectal cancer (CRC) patients. Radiomics based on high-throughput mining of quantitative medical imaging is an emerging field in recent years. However, the relationship among prognosis, radiomics features, and gene expression remains unknown. Methods: We retrospectively analyzed 141 patients (from study 1) diagnosed with CRC from February 2018 to October 2019 and randomly divided them into training (N = 99) and testing (N = 42) cohorts. Radiomics features in venous phase image were extracted from preoperative computed tomography (CT) images. Gene expression was detected by RNA-sequencing on tumor tissues. The least absolute shrinkage and selection operator (LASSO) regression model was used for selecting imaging features and building the radiomics model. A total of 45 CRC patients (study 2) with immunohistochemical (IHC) staining of CXCL8 diagnosed with CRC from January 2014 to October 2018 were included in the independent testing cohort. A clinical model was validated for prognosis prediction in prognostic testing cohort (163 CRC patients from 2014 to 2018, study 3). We performed a combined radiomics model that was composed of radiomics score, tumor stage, and CXCL8-derived radiomics model to make comparison with the clinical model. Results: In our study, we identified the CXCL8 as a hub gene in affecting prognosis, which is mainly through regulating cytokine-cytokine receptor interaction and neutrophil migration pathway. The radiomics model incorporated 12 radiomics features screened by LASSO according to CXCL8 expression in the training cohort and showed good performance in testing and IHC testing cohorts. Finally, the CXCL8-derived radiomics model combined with tumor stage performed high ability in predicting the prognosis of CRC patients in the prognostic testing cohort, with an area under the curve (AUC) of 0.774 [95% confidence interval (CI): 0.674-0.874]. Kaplan-Meier analysis of the overall survival probability in CRC patients stratified by combined model revealed that high-risk patients have a poor prognosis compared with low-risk patients (Log-rank P < 0.0001). Conclusion: We demonstrated that the radiomics model reflected by CXCL8 combined with tumor stage information is a reliable approach to predict the prognosis in CRC patients and has a potential ability in assisting clinical decision-making.

Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma

AIM

To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally advanced larynx and hypopharynx squamous cell carcinoma (SCC) receiving (chemo)radiotherapy.

MATERIALS AND METHODS

Patients with larynx and hypopharynx SCC treated with definitive (chemo)radiotherapy at a specialist cancer centre undergoing pre-treatment PET-CT between 2008 and 2017 were included. Tumour segmentation and radiomic analysis was performed using LIFEx software (University of Paris-Saclay, France). Data were assigned into training (80%) and validation (20%) cohorts adhering to TRIPOD guidelines. A random forest classifier was created for four predictive models using features determined by recursive feature elimination: (A) PET, (B) CT, (C) clinical, and (D) combined PET-CT parameters. Model performance was assessed using area under the curve (AUC) receiver operating characteristic (ROC) analysis.

RESULTS

Seventy-two patients (40 hypopharynx 32 larynx tumours) were included, mean age 61 (range 41-77) years, 50 (69%) were men. Forty-five (62.5%) had chemoradiotherapy, 27 (37.5%) had radiotherapy alone. Median follow-up 26 months (range 12-105 months). Twenty-seven (37.5%) patients progressed within 12 months. ROC AUC for models A, B, C, and D were 0.91, 0.94, 0.88, and 0.93 in training and 0.82, 0.72, 0.70, and 0.94 in validation cohorts. Parameters in model D were metabolic tumour volume (MTV), maximum CT value, minimum standardized uptake value (SUVmin), grey-level zone length matrix (GLZLM) small-zone low grey-level emphasis (SZLGE) and histogram kurtosis.

CONCLUSION

FDG PET-CT derived radiomic features are potential predictors of early disease progression in patients with locally advanced larynx and hypopharynx SCC.

Prediction of Human Papillomavirus Status and Overall Survival in Patients with Untreated Oropharyngeal Squamous Cell Carcinoma: Development and Validation of CT-Based Radiomics

BACKGROUND AND PURPOSE

Human papillomavirus is a prognostic marker for oropharyngeal squamous cell carcinoma. We aimed to determine the value of CT-based radiomics for predicting the human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

MATERIALS AND METHODS

Eighty-six patients with oropharyngeal squamous cell carcinoma were retrospectively collected and grouped into training (n = 61) and test (n = 25) sets. For human papillomavirus status and overall survival prediction, radiomics features were selected via a random forest-based algorithm and Cox regression analysis, respectively. Relevant features were used to build multivariate Cox regression models and calculate the radiomics score. Human papillomavirus status and overall survival prediction were assessed via the area under the curve and concordance index, respectively. The models were validated in the test and The Cancer Imaging Archive cohorts (n = 78).

RESULTS

For prediction of human papillomavirus status, radiomics features yielded areas under the curve of 0.865, 0.747, and 0.834 in the training, test, and validation sets, respectively. In the univariate Cox regression, the human papillomavirus status (positive: hazard ratio, 0.257; 95% CI, 0.09-0.7; P = .008), T-stage (≥III: hazard ratio, 3.66; 95% CI, 1.34-9.99; P = .011), and radiomics score (high-risk: hazard ratio, 3.72; 95% CI, 1.21-11.46; P = .022) were associated with overall survival. The addition of the radiomics score to the clinical Cox model increased the concordance index from 0.702 to 0.733 (P = .01). Validation yielded concordance indices of 0.866 and 0.720.

CONCLUSIONS

CT-based radiomics may be useful in predicting human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging

Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in these models. This review summarizes recent developments in the field of radiomics for AI in head and neck cancer. Prediction models for oncologic outcomes, treatment toxicity, and pathological findings have all been created. Exploratory studies are promising; however, validation studies that demonstrate consistency, reproducibility, and prognostic impact remain uncommon. Prospective clinical trials with standardized procedures are required for clinical translation.

Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI

OBJECTIVES

To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status.

STUDY DESIGN

Retrospective cohort study.

METHODS

Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status.

CONCLUSIONS

Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker.

LEVEL OF EVIDENCE

3 Laryngoscope, 131:E851-E856, 2021.

Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy

BACKGROUND

Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive pancreatic precursor lesions that can potentially develop into invasive pancreatic ductal adenocarcinoma. Currently, the International Consensus Guidelines (ICG) for IPMNs provides the basis for evaluating suspected IPMNs on computed tomography (CT) imaging. Despite using the ICG, it remains challenging to accurately predict whether IPMNs harbor high grade or invasive disease which would warrant surgical resection. A supplementary quantitative radiological tool, radiomics, may improve diagnostic accuracy of radiological evaluation of IPMNs. We hypothesized that using CT whole lesion radiomics features in conjunction with the ICG could improve the diagnostic accuracy of predicting IPMN histology.

AIM

To evaluate whole lesion CT radiomic analysis of IPMNs for predicting malignant histology compared to International Consensus Guidelines.

METHODS

Fifty-one subjects who had pancreatic surgical resection at our institution with histology demonstrating IPMN and available preoperative CT imaging were included in this retrospective cohort. Whole lesion semi-automated segmentation was performed on each preoperative CT using Healthmyne software (Healthmyne, Madison, WI). Thirty-nine relevant radiomic features were extracted from each lesion on each available contrast phase. Univariate analysis of the 39 radiomics features was performed for each contrast phase and values were compared between malignant and benign IPMN groups using logistic regression. Conventional quantitative and qualitative CT measurements were also compared between groups, via χ² (categorical) and Mann Whitney U (continuous) variables.

RESULTS

Twenty-nine subjects (15 males, age 71 ± 9 years) with high grade or invasive tumor histology comprised the "malignant" cohort, while 22 subjects (11 males, age 70 ± 7 years) with low grade tumor histology were included in the "benign" cohort. Radiomic analysis showed 18/39 precontrast, 19/39 arterial phase, and 21/39 venous phase features differentiated malignant from benign IPMNs (P < 0.05). Multivariate analysis including only ICG criteria yielded two significant variables: thickened and enhancing cyst wall and enhancing mural nodule < 5 mm with an AUC (95%CI) of 0.817 (0.709-0.926). Multivariable post contrast radiomics achieved an AUC (95%CI) of 0.87 (0.767-0.974) for a model including arterial phase radiomics features and 0.834 (0.716-0.953) for a model including venous phase radiomics features. Combined multivariable model including conventional variables and arterial phase radiomics features achieved an AUC (95%CI) of 0.93 (0.85-1.0) with a 5-fold cross validation AUC of 0.90.

CONCLUSION

Multi-phase CT radiomics evaluation could play a role in improving predictive capability in diagnosing malignancy in IPMNs. Future larger studies may help determine the clinical significance of our findings.

A Literature Review of the Potential Diagnostic Biomarkers of Head and Neck Neoplasms

Head and neck neoplasms have a poor prognosis because of their late diagnosis. Finding a biomarker to detect these tumors in an early phase could improve the prognosis and survival rate. This literature review provides an overview of biomarkers, covering the different -omics fields to diagnose head and neck neoplasms in the early phase. To date, not a single biomarker, nor a panel of biomarkers for the detection of head and neck tumors has been detected with clinical applicability. Limitations for the clinical implementation of the investigated biomarkers are mainly the heterogeneity of the study groups (e.g., small population in which the biomarker was tested, and/or only including high-risk populations) and a low sensitivity and/or specificity of the biomarkers under study. Further research on biomarkers to diagnose head and neck neoplasms in an early stage, is therefore needed.

Radiomic biomarkers for head and neck squamous cell carcinoma

Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell carcinoma (HNSCC). A major limitation of tissue- and blood-derived tumor markers is the lack of spatial resolution to image tumor heterogeneity. Tissue markers derived from tumor biopsies usually represent only a small tumor subregion at a single timepoint and are therefore often not representative of the tumors' biology or the biological alterations during and after treatment. Similarly, liquid biopsies give an overall picture of the tumors' secreted factors but completely lack any spatial resolution. Radiomics has the potential to give complete three-dimensional information about the tumor. We conducted a comprehensive literature search to assess the correlation of radiomics to tumor biology and treatment outcome in HNSCC and to assess current limitations of the radiomic biomarkers. In total, 25 studies that explored the ability of radiomics to predict tumor biology and phenotype in HNSCC and 28 studies that explored radiomics to predict post-treatment events were identified. Out of these 53 studies, only three failed to show a significant correlation. The major technical challenges are currently artifacts due to metal implants, non-standardized contrast injection, and delineation uncertainties. All studies to date were retrospective and none of the above-mentioned radiomics signatures have been validated in an independent cohort using an independent software implementation, which shows that transferability due to the numerous technical challenges is currently a major limitation. However, radiomics is a very young field and these studies hopefully pave the way for clinical implementation of radiomics for HNSCC in the future.

CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma

Purpose

To determine the performance of CT-based radiomic features for noninvasive prediction of histopathologic features of tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus status in head and neck squamous cell carcinoma (HNSCC).

Materials and Methods

In this retrospective study, which was approved by the local institutional ethics committee, CT images and clinical data from patients with pathologically proven HNSCC from The Cancer Genome Atlas (n = 113) and an institutional test cohort (n = 71) were analyzed. A machine learning model was trained with 2131 extracted radiomic features to predict tumor histopathologic characteristics. In the model, principal component analysis was used for dimensionality reduction, and regularized regression was used for classification.

Results

The trained radiomic model demonstrated moderate capability of predicting HNSCC features. In the training cohort and the test cohort, the model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.81) and 0.66 (95% CI: 0.45, 0.84), respectively, for tumor grade; a mean AUC of 0.64 (95% CI: 0.55, 0.62) and 0.70 (95% CI: 0.47, 0.89), respectively, for perineural invasion; a mean AUC of 0.69 (95% CI: 0.56, 0.81) and 0.65 (95% CI: 0.38, 0.87), respectively, for lymphovascular invasion; a mean AUC of 0.77 (95% CI: 0.65, 0.88) and 0.67 (95% CI: 0.15, 0.80), respectively, for extracapsular spread; and a mean AUC of 0.71 (95% CI: 0.29, 1.0) and 0.80 (95% CI: 0.65, 0.92), respectively, for human papillomavirus status.

Conclusion

Radiomic CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC.Supplemental material is available for this article.© RSNA, 2020.

Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas

Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the “-omics” concept for the broader field of head and neck cancer – “Radiomics”. This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.

Radiomics: A primer for the radiation oncologist

PURPOSE

Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy.

METHODS

A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review.

RESULTS

A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic.

CONCLUSION

Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases

OBJECTIVES

To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.

MATERIALS AND METHODS

A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models.

RESULTS

Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10^-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10^-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37).

CONCLUSION

Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Radiomic analysis for response assessment in advanced head and neck cancers, a distant dream or an inevitable reality? A systematic review of the current level of evidence

OBJECTIVE

The recent increase in publications on radiomic analysis as means to produce diagnostic and predictive biomarkers in head and neck cancers (HNCC) reveal complicated and often conflicting results. The objective of this paper is to systematically review the published data, and evaluate the current level of evidence accumulated that would determine clinical application.

METHODS

Data sources: Articles in the English language available on the Ovid-MEDLINE and Embase databases were used for the literature search. Study selection:Studies which evaluated the role of radiomics as a predictive or prognostic tool for response assessment in HNCC were included in this review.Study appraisal and synthesis methods: The authors set-out to perform a meta-analysis, however given the small number of studies retrieved that presented adequate data, combined with excessive methodological heterogeneity, we could only perform a structured descriptive systematic review summarizing the key findings. Independent extraction of articles was performed by two authors using predefined data fields and any disagreement was resolved by consensus.

RESULTS

Though most papers concluded that radiomics is an effective predictive and prognostic biomarker in the management of HNCC, significant heterogeneity exists in the study methodology and statistical modelling; thus precluding accurate mathematical comparison or the ability to make clear recommendations going forwards. Moreover, most studies have not been validated and the reproducibility of their results will be a challenge.

CONCLUSION

Until robust external validation studies on the reproducibility and accuracy of radiomic analysis methods on HNCC are carried out, the current level of evidence remains low, with the authors advising caution against hasty implementation of these tools in the multidisciplinary clinic.

ADVANCES IN KNOWLEDGE

This review is the first attempt to critically analyze the merits and demerits of currently published literature on tumour heterogeneity studies in HNCC, and identifies specific loop holes that need to be addressed by research groups, for a meaningful clinical translation of this potential biomarker.

Hypoxia-Targeting Multifunctional Nanoparticles for Sensitized Chemotherapy and Phototherapy in Head and Neck Squamous Cell Carcinoma

Purpose

Chemotherapy in head and neck squamous cell carcinoma (HNSCC) has many systemic side effects, as well as hypoxia-induced chemoresistance. To reduce side effects and enhance chemosensitivity are urgently needed.

Methods

We synthesized a drug delivery system (named CECMa NPs) based on cisplatin (CDDP) and metformin (chemotherapeutic sensitizer), of which chlorin e6 (Ce6) and polyethylene glycol diamine (PEG) were synthesized as the shell, an anti-LDLR antibody (which can target to hypoxic tumor cells) was modified on the surface to achieve tumor targeting.

Results

The NPs possessed a great synergistic effect of chemotherapy and phototherapy. After laser stimulation, both CDDP and metformin can be released in situ to achieve anti-tumor effects. Meanwhile, PDT and PTT triggered by a laser have anticancer effects. Furthermore, compared with free cisplatin, CECMa exhibits less systemic toxicity with laser irradiation in the xenograft mouse tumor model.

Conclusion

CECMa effectively destroyed the tumors via hypoxia targeting multimodal therapy both in vitro and in vivo, thereby providing a novel strategy for targeting head and neck squamous cell carcinoma.