Radiomic Application for Head and Neck Squamocellular Tumor: Systematic Review.

Abstract: Radiomics represents the convergence of artificial intelligence and radiological data analysis, primarily applied in the diagnosis and treatment of cancer. In the head and neck region, squamous cell carcinoma is the most prevalent type of tumor. Recent radiomics research has revealed that specific bio-imaging characteristics correlate with various molecular features of Head and Neck Squamous Cell Carcinoma (HNSCC), particularly Human Papillomavirus (HPV). These tumors typically present a unique phenotype, often affecting younger patients, and show a favorable response to radiation therapy. This study provides a systematic review of the literature, summarizing the application of radiomics in the head and neck region. It offers a comprehensive analysis of radiomics-based studies on HNSCC, evaluating its potential for tumor evaluation, risk stratification, and outcome prediction in head and neck cancer treatment.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

The Diagnostic Performance of Magnetic Resonance Imaging in the Categorization of Pediatric Neck Lymph Nodes: Radiologic and Pathologic Correlations.

BACKGROUND/AIM: To present MRI features of neck lymph nodes in benign and malignant conditions in the pediatric population. MATERIALS AND METHODS: MRIs of the neck of 51 patients 1 to 18 years old (40 boys, 11 girls [10.08±4.73]) with lymph node biopsy were retrospectively analyzed. Those were grouped as benign including reactive (27 [52.9%]) and lymphadenitis (11 [21.6%]), and malignant (13 [25.5%]). The groups were evaluated multiparametrically in terms of quantitative and qualitative variables. RESULTS: The long axis, short axis, area, and apparent diffusion coefficient (ADC) values of the largest lymph node were 21 (17 to 24) mm, 14 (12 to 18) mm, 228.60 (144.79 to 351.82) mm 2 , 2531 (2457 to 2714) mm 2 /s for reactive, 24 (19 to 27) mm, 15 (11 to 20) mm, 271.80 (231.43 to 412.20) mm 2 , 2534 (2425 to 2594) mm 2 /s for lymphadenitis, 27 (23.50 to 31.50) mm, 20 (15 to 22) mm, 377.08 (260.47 to 530.94) mm 2 , 2337 (2254 to 2466) mm 2 /s for malignant, respectively. Statistical analysis of our data suggests that the following parameters are associated with a higher likelihood of malignancy: long axis >22 mm, short axis >16 mm, area >319 cm 2 , ADC value <2367 mm 2 /s, and supraclavicular location. Perinodal and nodal heterogeneity, posterior cervical triangle location are common in lymphadenitis ( P <0.001). Reactive lymph nodes are distributed symmetrically in both neck halves ( P <0.001). CONCLUSION: In the MRI-based approach to lymph nodes, not only long axis, short axis, surface area, and ADC, but also location, distribution, perinodal, and nodal heterogeneity should be used.

Impact Of PET/CT On Treatment In Patients With Head And Neck Squamous Cell Carcinoma.

<b><br>Introduction:</b> Although PET/CT is effective for staging HNSCC, its impact on patient management is somewhat controversial. For this reason, we considered it necessary to carry out a study in order to verify whether PET/CT helps to improve the prognosis and treatment in patients. This study was designed to address the impact of PET-FDG imaging when used alongside CT in the staging and therapeutic management of patients with HNSCC.</br> <b><br>Material and methods:</b> Data was collected from 169 patients diagnosed with HNSCC with both CT and PET/CT (performed within a maximum of 30 days of each other). It was evaluated whether discrepancies in the diagnosis of the two imaging tests had impacted the treatment.</br> <b><br>Results:</b> The combined use of CT and PET/CT led to a change in the treatment of 67 patients, who represented 39.7% of the sample. In 27.2% of cases, it entailed a change in the type of treatment which the patient received. In 3.0% of the cases, using both diagnostic tests led to modifications of the therapeutic intention of our patients.</br> <b><br>Conclusions:</b> Using PET/CT in addition to the conventional imaging method in staging resulted in more successful staging and more appropriate therapeutic decision-making.</br>.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images.

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction.

Cancer begins when healthy cells change and grow out of control, forming a mass called a tumor. Head and neck (H&N) cancers usually develop in or around the head and neck, including the mouth (oral cavity), nose and sinuses, throat (pharynx), and voice box (larynx). 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64.7%). FDG-PET/CT imaging is often used for early diagnosis and staging of H&N tumors, thus improving these patients' survival rates. This work presents a novel 3D-Inception-Residual aided with 3D depth-wise convolution and squeeze and excitation block. We introduce a 3D depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder (3D-IncNet), which not only helps to recalibrate the channel-wise features but adaptively through explicit inter-dependencies modeling but also integrate the coarse and fine features resulting in accurate tumor segmentation. We further demonstrate the effectiveness of inception-residual encoder-decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. We applied random forest for survival prediction on deep, clinical, and radiomics features. Experiments are conducted on the benchmark HECKTOR21 challenge, which showed significantly better performance by surpassing the state-of-the-artwork and achieved 0.836 and 0.811 concordance index and dice scores, respectively. We made the model and code publicly available.

The utility of ultrasonographic surveillance in management of a presumed branchial cleft cyst later confirmed HPV-associated oropharyngeal cancer.

While branchial cleft cysts are often considered benign pathologies, the literature discusses cases of squamous cell carcinoma (SCC) arising from these cystic lesions as either a primary or metastatic tumor. We illustrate our institutional experience and review the current literature to identify recommendations for best diagnostic, surveillance, and treatment guidelines for SCC identified in a branchial cleft cyst. A 61-year-old male presented with a right sided neck mass, with suspicion of a branchial cleft cyst due to benign findings on fine needle aspiration. Following surgical excision, a focus of SCC was found on surgical pathology. Despite PET/CT and flexible laryngoscopy, no primary tumor was identified prompting routine surveillance every 3 months with cervical ultrasonography and flexible nasolaryngoscopy. Two and a half years following his initial presentation, pathologic right level II lymphadenopathy was detected on ultrasound without evidence of primary tumor. Subsequent transoral robotic surgery with right tonsillectomy and partial pharyngectomy, with right lateral neck dissection revealed a diagnosis of pT1N1 HPV-HNSCC and he was referred for adjuvant chemotherapy and radiation. To our knowledge there are less than 10 cases of confirmed HPV-associated oropharyngeal SCC arising from a branchial cleft cyst. Here we demonstrate the utility of ultrasound as a surveillance tool and emphasize a higher index of suspicion for carcinoma in adult patients with cystic neck masses.

Oxygen-enhanced MRI assessment of tumour hypoxia in head and neck cancer is feasible and well tolerated in the clinical setting.

BACKGROUND: Tumour hypoxia is a recognised cause of radiotherapy treatment resistance in head and neck squamous cell carcinoma (HNSCC). Current positron emission tomography-based hypoxia imaging techniques are not routinely available in many centres. We investigated if an alternative technique called oxygen-enhanced magnetic resonance imaging (OE-MRI) could be performed in HNSCC. METHODS: A volumetric OE-MRI protocol for dynamic T1 relaxation time mapping was implemented on 1.5-T clinical scanners. Participants were scanned breathing room air and during high-flow oxygen administration. Oxygen-induced changes in T1 times (ΔT1) and R2* rates (ΔR2*) were measured in malignant tissue and healthy organs. Unequal variance t-test was used. Patients were surveyed on their experience of the OE-MRI protocol. RESULTS: Fifteen patients with HNSCC (median age 59 years, range 38 to 76) and 10 non-HNSCC subjects (median age 46.5 years, range 32 to 62) were scanned; the OE-MRI acquisition took less than 10 min and was well tolerated. Fifteen histologically confirmed primary tumours and 41 malignant nodal masses were identified. Median (range) of ΔT1 times and hypoxic fraction estimates for primary tumours were -3.5% (-7.0 to -0.3%) and 30.7% (6.5 to 78.6%) respectively. Radiotherapy-responsive and radiotherapy-resistant primary tumours had mean estimated hypoxic fractions of 36.8% (95% confidence interval [CI] 17.4 to 56.2%) and 59.0% (95% CI 44.6 to 73.3%), respectively (p = 0.111). CONCLUSIONS: We present a well-tolerated implementation of dynamic, volumetric OE-MRI of the head and neck region allowing discernment of differing oxygen responses within biopsy-confirmed HNSCC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04724096 . Registered on 26 January 2021. RELEVANCE STATEMENT: MRI of tumour hypoxia in head and neck cancer using routine clinical equipment is feasible and well tolerated and allows estimates of tumour hypoxic fractions in less than ten minutes. KEY POINTS: • Oxygen-enhanced MRI (OE-MRI) can estimate tumour hypoxic fractions in ten-minute scanning. • OE-MRI may be incorporable into routine clinical tumour imaging. • OE-MRI has the potential to predict outcomes after radiotherapy treatment.

A radiomics model to predict γδ T-cell abundance and overall survival in head and neck squamous cell carcinoma.

γδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T-cell abundance using gradient boosting machine. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T-cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T-cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T-cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.

RADCURE: An open-source head and neck cancer CT dataset for clinical radiation therapy insights.

PURPOSE: This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. ACQUISITION AND VALIDATION METHODS: RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ-at-risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom-built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system (Tumor-Node-Metastasis Classification System of Malignant Tumors). The median patient age is 63, with the final dataset including 80% males. Half of the cohort is diagnosed with oropharyngeal cancer, while laryngeal, nasopharyngeal, and hypopharyngeal cancers account for 25%, 12%, and 5% of cases, respectively. The median duration of follow-up is five years, with 60% of the cohort surviving until the last follow-up point. DATA FORMAT AND USAGE NOTES: The dataset provides images and contours in DICOM CT and RT-STRUCT formats, respectively. We have standardized the nomenclature for individual contours-such as the gross primary tumor, gross nodal volumes, and 19 organs-at-risk-to enhance the RT-STRUCT files' utility. Accompanying demographic, clinical, and treatment data are supplied in a comma-separated values (CSV) file format. This comprehensive dataset is publicly accessible via The Cancer Imaging Archive. POTENTIAL APPLICATIONS: RADCURE's amalgamation of imaging, clinical, demographic, and treatment data renders it an invaluable resource for a broad spectrum of radiomics image analysis research endeavors. Researchers can utilize this dataset to advance routine clinical procedures using machine learning or artificial intelligence, to identify new non-invasive biomarkers, or to forge prognostic models.

Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma.

Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.

Identification of new head and neck squamous cell carcinoma molecular imaging targets.

OBJECTIVES: Intraoperative fluorescence imaging (FI) of head and neck squamous cell carcinoma (HNSCC) is performed to identify tumour-positive surgical margins, currently using epidermal growth factor receptor (EGFR) as imaging target. EGFR, not exclusively present in HNSCC, may result in non-specific tracer accumulation in normal tissues. We aimed to identify new potential HNSCC FI targets. MATERIALS AND METHODS: Publicly available transcriptomic data were collected, and a biostatistical method (Transcriptional Adaptation to Copy Number Alterations (TACNA)-profiling) was applied. TACNA-profiling captures downstream effects of CNAs on mRNA levels, which may translate to protein-level overexpression. Overexpressed genes were identified by comparing HNSCC versus healthy oral mucosa. Potential targets, selected based on overexpression and plasma membrane expression, were immunohistochemically stained. Expression was compared to EGFR on paired biopsies of HNSCC, adjacent macroscopically suspicious mucosa, and healthy mucosa. RESULTS: TACNA-profiling was applied on 111 healthy oral mucosa and 410 HNSCC samples, comparing expression levels of 19,635 genes. The newly identified targets were glucose transporter-1 (GLUT-1), placental cadherin (P-cadherin), monocarboxylate transporter-1 (MCT-1), and neural/glial antigen-2 (NG2), and were evaluated by IHC on samples of 31 patients. GLUT-1 was expressed in 100 % (median; range: 60-100 %) of tumour cells, P-cadherin in 100 % (50-100 %), EGFR in 70 % (0-100 %), MCT-1 in 30 % (0-100 %), and NG2 in 10 % (0-70 %). GLUT-1 and P-cadherin showed higher expression than EGFR (p < 0.001 and p = 0.015). CONCLUSIONS: The immunohistochemical confirmation of TACNA-profiling results showed significantly higher GLUT-1 and P-cadherin expression than EGFR, warranting further investigation as HNSCC FI targets.

Association of circulating tumor HPV16DNA levels and quantitative PET parameters in patients with HPV-positive head and neck squamous cell carcinoma.

Circulating tumor DNA (ctDNA), which circulates in the blood after being shed from cancer cells in the body, has recently gained attention as an excellent tumor marker. The purpose of this study was to evaluate whether ct human papillomavirus (HPV) 16 DNA (ctHPV16DNA) levels were associated with quantitative PET parameters in patients with HPV-positive head and neck (HN) squamous cell carcinoma (SCC). Fifty patients with oropharyngeal SCC (OPSCC) and 5 with SCC of unknown primary (SCCUP) before treatment were included. They all underwent blood sampling to test ctHPV16DNA levels and FDG PET-CT examinations. Quantitative PET parameters included SUVmax, metabolic tumor volume (MTV), MTV of whole-body lesions (wbMTV), and 56 texture features. ctHPV16DNA levels were compared to texture features of primary tumors in OPSCC patients (Group A) or the largest primary or metastatic lymph node lesions in OPSCC and SCCUP patients (Group B) and to other PET parameters. Spearman rank correlation test and multiple regression analysis were used to confirm the associations between ctHPV16DNA levels and PET parameters. ctHPV16DNA levels moderately correlated with wbMTV, but not with SUVmax or MTV in Groups A and B. ctHPV16DNA levels exhibited a weak negative correlation with low gray-level zone emphasis in Groups A and B. Multiple regression analysis revealed that wbMTV and high gray-level zone emphasis were the significant factors for ctHPV16DNA levels in Group B. These results were not observed in Group A. This study demonstrated that ctHPV16DNA levels correlated with the whole-body tumor burden and tumor heterogeneity visualized on FDG PET-CT in patients with HPV-positive HNSCC.

Ultrasound Muscle Evaluation for Predicting the Prognosis of Patients with Head and Neck Cancer: A Large-Scale and Multicenter Prospective Study.

Head and neck cancer (HNC) is a prevalent and aggressive form of cancer with high mortality rates and significant implications for nutritional status. Accurate assessment of malnutrition in patients with HNC is crucial for optimizing treatment outcomes and improving survival rates. This study aimed to evaluate the use of ultrasound techniques for predicting nutritional status, malnutrition, and cancer outcomes in patients with HNC. A total of 494 patients with HNC were included in this cross-sectional observational study. Various tools and body composition measurements, including muscle mass and adipose tissue ultrasound evaluations, were implemented. Using regression models, we mainly found that high levels of RF-CSA (rectus femoris cross-sectional area) were associated with a decreased risk of malnutrition (as defined with GLIM criteria (OR = 0.81, 95% CI: 0.68-0.98); as defined with PG-SGA (OR = 0.78, 95% CI: 0.62-0.98)) and sarcopenia (OR = 0.64, 95% CI: 0.49-0.82) after being adjusted for age, sex, and BMI. To predict the importance of muscle mass ultrasound variables on the risk of mortality, a nomogram, a random forest, and decision tree models were conducted. RF-CSA was the most important variable under the random forest model. The obtained C-index for the nomogram was 0.704, and the Brier score was 16.8. With an RF-CSA < 2.7 (AUC of 0.653 (0.59-0.77)) as a split, the decision tree model classified up to 68% of patients as possessing a high probability of survival. According to the cut-off value of 2.7 cm2, patients with a low RF-CSA value lower than 2.7 cm2 had worse survival rates (p < 0.001). The findings of this study highlight the importance of implementing ultrasound tools, for accurate diagnoses and monitoring of malnutrition in patients with HNC. Adipose tissue ultrasound measurements were only weakly associated with malnutrition and not with sarcopenia, indicating that muscle mass is a more important indicator of overall health and nutritional status. These results have the potential to improve survival rates and quality of life by enabling early intervention and personalized nutritional management.

Biomechanical imaging biomarker during chemoradiotherapy predicts treatment response in head and neck squamous cell carcinoma.

Objective. For response-adapted adaptive radiotherapy (R-ART), promising biomarkers are needed to predict post-radiotherapy (post-RT) responses using routine clinical information obtained during RT. In this study, a patient-specific biomechanical model (BM) of the head and neck squamous cell carcinoma (HNSCC) was proposed using the pre-RT maximum standardized uptake value (SUVmax) of18F-fluorodeoxyglucose (FDG) and tumor structural changes during RT as evaluated using computed tomography (CT). In addition, we evaluated the predictive performance of BM-driven imaging biomarkers for the treatment response of patients with HNSCC who underwent concurrent chemoradiotherapy (CCRT).Approach. Patients with histologically confirmed HNSCC treated with definitive CCRT were enrolled in this study. All patients underwent CT two times as follows: before the start of RT (pre-RT) and 3 weeks after the start of RT (mid-RT). Among these patients, 67 patients who underwent positron emission tomography/CT during the pre-RT period were included in the final analysis. The locoregional control (LC), progression-free survival (PFS), and overall survival (OS) prediction performances of whole tumor stress change (TS) between pre- and mid-RT computed using BM were assessed using univariate, multivariate, and Kaplan-Meier survival curve analyses, respectively. Furthermore, performance was compared with the pre and post-RT SUVmax, tumor volume reduction rate (TVRR) during RT, and other clinical prognostic factors.Main results. For both univariate, multivariate, and survival curve analyses, the significant prognostic factors were as follows (p< 0.05): TS and TVRR for LC; TS and pre-RT FDG-SUVmaxfor PFS; and TS only for OS. In addition, for 2 year LC, PFS, and OS prediction, TS showed a comparable predictive performance to post-RT FDG-SUVmax.Significance. BM-driven TS is an effective prognostic factor for tumor treatment response after CCRT. The proposed method can be a feasible functional imaging biomarker that can be acquired during RT using only routine clinical data and may provide useful information for decision-making during R-ART.

Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. Methods: This retrospective study consisted of 611 18F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on 18F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Results: Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively (P < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses (P < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. Conclusion: The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on 18F-FDG and PSMA PET/CT scans.

Valor pronóstico de los parámetros hematológicos y los parámetros metabólicos de la PET/TC con [18F]FDG en el cáncer de cabeza y cuello / Prognostic value of haematological parameters and [18F]FDG PET/CT metabolic parameters in head and neck cancer

Objetivo Determinar la utilidad de los cocientes neutrófilos/linfocitos (N/L) y plaquetas/linfocitos (P/L), así como de parámetros cuantitativos de la PET/TC con [18F]FDG, como factores pronósticos para la supervivencia global (SG), la supervivencia cáncer específica (SCE) y la supervivencia libre de progresión (SLP) en pacientes con carcinoma escamoso de cabeza y cuello (CyC) Material y métodos Se valoraron retrospectivamente 66 pacientes (56 hombres) diagnosticados de CyC durante un intervalo de 8años. Se determinaron los parámetros SUV máximo (SUVmax), volumen metabólico tumoral (MTV) y glucólisis tumoral total (TLG) del estudio PET/TC al diagnóstico. Tras tratamiento con quimiorradioterapia, se valoró la supervivencia de los pacientes. El modelo de regresión de Cox y el método de Kaplan-Meier se utilizaron para analizar factores pronósticos y curvas de supervivencia. Resultados El seguimiento medio fue de 50,4meses, produciéndose 39 recurrencias-progresiones y 39 fallecimientos. En el análisis univariante los parámetros metabólicos, excepto el SUVmax, fueron factores predictivos para las tres supervivencias, y los dos parámetros sanguíneos lo fueron para la SG y la SCE. La TLG fue el único factor predictivo en el análisis multivariante. Las tres curvas de supervivencias fueron significativamente diferentes para los parámetros metabólicos y la curva de SG para el cociente N/L. Se apreciaron correlaciones entre el cociente N/L, el MTV y la TLG. No se demostraron correlaciones entre el cociente P/L y los parámetros metabólicos. Conclusión El uso de marcadores hematológicos y metabólicos permitiría identificar pacientes con un alto riesgo de recurrencias y pobre supervivencia e individualizar el tratamiento aplicando terapias más agresivas (AU)

Aim To determine the usefulness of neutrophil/lymphocyte (N/L) and platelet/lymphocyte (P/L) ratios as well as quantitative [18F]FDG PET/CT parameters as prognostic factors for overall survival (OS), cancer-specific survival (CSS) and progression-free survival (PFS) in patients with head and neck squamous cell carcinoma (HyN). Material and methods Sixty-six patients (56 men) diagnosed with HyN carcinoma were retrospectively assessed over an 8-year interval. Maximum SUV (SUVmax), metabolic tumour volume (MTV) and total lesion glycolysis (TLG) parameters were determined from the PET/CT study at diagnosis. After treatment with chemoradiotherapy, patient survival was assessed. The Cox regression model and the Kaplan-Meier method were used to analyse prognostic factors and survival curves. Results Median follow-up was 50.4months, with 39 recurrences-progressions and 39 deaths. In the univariate analysis, metabolic parameters, except SUVmax, were predictive factors for all three survivals and the two blood parameters were predictive for OS and EFS. TLG was the only predictive factor in the multivariate analysis. The three survival curves were significantly different for the metabolic parameters and the OS curve for the N/L ratio. Correlations were seen between N/L ratio, MTV and TLG. No correlations were demonstrated between P/L ratio and metabolic parameters. Conclusion The use of haematological and metabolic markers would allow to identify patients with a high risk of recurrences and poor survival and to individualise treatment by applying more aggressive therapies (AU)

Multi-organ segmentation of organ-at-risk (OAR's) of head and neck site using ensemble learning technique.

INTRODUCTION: This paper presents a novel approach to automate the segmentation of Organ-at-Risk (OAR) in Head and Neck cancer patients using Deep Learning models combined with Ensemble Learning techniques. The study aims to improve the accuracy and efficiency of OAR segmentation, essential for radiotherapy treatment planning. METHODS: The dataset comprised computed tomography (CT) scans of 182 patients in DICOM format, obtained from an institutional image bank. Experienced Radiation Oncologists manually segmented seven OARs for each scan. Two models, 3D U-Net and 3D DenseNet-FCN, were trained on reduced CT scans (192 × 192 x 128) due to memory limitations. Ensemble Learning techniques were employed to enhance accuracy and segmentation metrics. Testing was conducted on 78 patients from the institutional dataset and an open-source dataset (TCGA-HNSC and Head-Neck Cetuximab) consisting of 31 patient scans. RESULTS: Using the Ensemble Learning technique, the average dice similarity coefficient for OARs ranged from 0.990 to 0.994, indicating high segmentation accuracy. The 95% Hausdorff distance (mm) ranged from 1.3 to 2.1, demonstrating precise segmentation boundaries. CONCLUSION: The proposed automated segmentation method achieved efficient and accurate OAR segmentation, surpassing human expert performance in terms of time and accuracy. IMPLICATIONS FOR PRACTICE: This approach has implications for improving treatment planning and patient care in radiotherapy. By reducing manual segmentation reliance, the proposed method offers significant time savings and potential improvements in treatment planning efficiency and precision for head and neck cancer patients.

Macroscopy of specimens from the head and neck.

Macroscopic examination of surgical resections from the head and neck may be difficult due to the complex anatomy of this area. Recognition of normal anatomical structures is essential for accurate assessment of the extent of a disease process. Communication with the surgical team, correct specimen orientation and sampling are critical for assessment and the importance of radiological and clinical correlation is emphasised. Tumour involvement at each subsite is highlighted with reference to where there are implications on pathological staging and the potential need for adjuvant therapy.