A study on the correlation of Doppler ultrasound in the diagnosis of cervical lymph nodes in patients with laryngeal and hypopharyngeal cancers: An observational study.

The objective was to assess the diagnostic efficacy of Doppler ultrasound in detecting cervical lymph nodes in patients diagnosed with laryngeal and hypopharyngeal cancers. Patients undergoing surgery for laryngeal and hypopharyngeal cancers in the Otolaryngology Department from January 2021 to January 2023 were included. Two groups, with equal numbers, underwent ultrasound examination and intensive CT examination in the experimental and control groups, respectively, along with routine cervical lymph node dissection. A resident with over 6 years of clinical experience in the otolaryngology department performed routine bilateral cervical lymph node palpation. Sensitivity, specificity, and validity were compared among different examination methods. The McNemar test assessed specificity and sensitivity between palpation, color Doppler ultrasonography, and enhanced CT, while the Kappa concordance test evaluated the concordance between the 2 examination methods. Data were statistically analyzed using SPSS 23.0. Palpation showed a diagnostic sensitivity (DS) of 52.83% and specificity of 91.11% for all patients with cervical lymph node metastasis. Ultrasonography demonstrated a DS of 77.78% and specificity of 81.82% in patients with cervical lymph node metastasis, while intensive CT had a DS of 75.86% and specificity of 60.00%. Statistical significance (P < .05) was observed in the sensitivity between palpation and ultrasonography, and between palpation and enhanced CT. The specificity between enhanced CT and ultrasonography (P = .021) and between palpation and enhanced CT scan (P = .003) both showed statistical significance (P < .05). Doppler ultrasound yields diagnostic results highly consistent with pathological diagnoses in patients with laryngeal and hypopharyngeal cancers. Utilizing Doppler ultrasound can enhance the accuracy of diagnosing these cancers, aiding physicians in devising more suitable treatment plans for patients.

The clinical utility of diffusion-weighted imaging in diagnosing and predicting treatment response of laryngeal and hypopharyngeal carcinoma: A systematic review and meta-analysis.

PURPOSE: Laryngeal and Hypopharyngeal Carcinomas (LC/HPC) constitute about 24 % of head and neck cancers, causing more than 90,000 annual deaths worldwide. Diffusion-Weighted Imaging (DWI), is currently widely studied in oncologic imaging and can aid in distinguishing cellular tumors from other tissues. Our objective was to review the effectiveness of DWI in three areas: diagnosing, predicting prognosis, and predicting treatment response in patients with LC/HPC. METHODS: A systematic search was conducted in PubMed, Web of Science, and Embase. A meta-analysis by calculating Standardized Mean Difference (SMD) and 95 % Confidence Interval (CI) was conducted on diagnostic studies. RESULTS: A total of 16 studies were included. All diagnostic studies (n = 9) were able to differentiate between the LC/HPC and other benign laryngeal/hypopharyngeal lesions. These studies found that LC/HPC had lower Apparent Diffusion Coefficient (ADC) values than non-cancerous lesions. Our meta-analysis of 7 diagnostic studies, that provided ADC values of malignant and non-malignant tissues, demonstrated significantly lower ADC values in LC/HPC compared to non-malignant lesions (SMD = -1.71, 95 %CI: [-2.00, -1.42], ADC cut-off = 1.2 × 103 mm2/s). Furthermore, among the studies predicting prognosis, 67 % (4/6) accurately predicted outcomes based on pretreatment ADC values. Similarly, among studies predicting treatment response, 50 % (2/4) successfully predicted outcomes based on pretreatment ADC values. Overall, the studies that looked at prognosis or treatment response in LC/HPC found a positive correlation between pretreatment ADC values in larynx/hypopharynx and favorable outcomes. CONCLUSION: DWI aids significantly in the LC/HPC diagnosis. However, further research is needed to establish DWI's reliability in predicting prognosis and treatment response in patients with LC/HPC.

Diagnostic value of 18F-FDG PET/CT versus diffusion-weighted MRI in detection of residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal squamous cell carcinoma: A prospective study.

BACKGROUND: Despite advances in treatment, residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal squamous cell carcinoma (SCC) remain a challenge in clinical management and require accurate and timely detection for optimal salvage therapy. This study aimed to compare the diagnostic value of Fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. METHODS: A prospective study was conducted on 30 patients who presented with new symptoms after definitive (chemo) radiotherapy for laryngeal (n = 21) and hypopharyngeal (n = 9) carcinoma. Both 18F-FDG PET/CT and DW-MRI were performed and histopathologic analysis served as the standard of reference. RESULTS: Histopathology showed 20 patients as positive and 10 as negative for tumors. 18F-FDG PET/CT detected all tumors correctly but was falsely positive in one case. DW-MRI detected tumors in 18 out of 20 positive patients and correctly excluded tumors in all negative patients. The sensitivity and specificity of 18F-FDG PET/CT were 100% and 90%, respectively, while the values for DW-MRI were 90% and 100%, respectively. CONCLUSIONS: The study concludes that 18F-FDG PET/CT is slightly superior to DW-MRI in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. The combined use of 18F-FDG PET/CT and DW-MRI can potentially improve specificity in therapy response evaluation.

Improved delineation with diffusion weighted imaging for laryngeal and hypopharyngeal tumors validated with pathology.

OBJECTIVE: This study aims to determine the added value of a geometrically accurate diffusion-weighted (DW-) MRI sequence on the accuracy of gross tumor volume (GTV) delineations, using pathological tumor delineations as a ground truth. METHODS: Sixteen patients with laryngeal or hypopharyngeal carcinoma were included. After total laryngectomy, the specimen was cut into slices. Photographs of these slices were stacked to create a 3D digital specimen reconstruction, which was registered to the in vivo imaging. The pathological tumor (tumorHE) was delineated on the specimen reconstruction. Six observers delineated all tumors twice: once with only anatomical MR imaging, and once (a few weeks later) when DW sequences were also provided. The majority voting delineation of session one (GTVMRI) and session two (GTVDW-MRI), as well as the clinical target volumes (CTVs), were compared to the tumorHE. RESULTS: The mean tumorHE volume was 11.1 cm3, compared to a mean GTVMRI volume of 18.5 cm3 and a mean GTVDW-MRI volume of 15.7 cm3. The median sensitivity (tumor coverage) was comparable between sessions: 0.93 (range: 0.61-0.99) for the GTVMRI and 0.91 (range: 0.53-1.00) for the GTVDW-MRI. The CTV volume also decreased when DWI was available, with a mean CTVMR of 47.1 cm3 and a mean CTVDW-MRI of 41.4 cm3. Complete tumor coverage was achieved in 15 and 14 tumors, respectively. CONCLUSION: GTV delineations based on anatomical MR imaging tend to overestimate the tumor volume. The availability of the geometrically accurate DW sequence reduces the GTV overestimation and thereby CTV volumes, while maintaining acceptable tumor coverage.

CT and MR anatomy of the larynx and hypopharynx.

Imaging of the larynx and hypopharynx is frequently requested to assess the extent of neoplasms beyond the field of view of endoscopic evaluation. The combination of optical and cross-sectional imaging allows tumors to be classified according to AJCC/UICC guidelines. A thorough understanding of laryngeal and hypopharyngeal anatomy is crucial to guide the radiological eye along the possible pathways of the spread of diseases and to guide differential diagnoses. Computed tomography (CT) has been the first cross-sectional imaging technique used to evaluate the larynx and hypopharynx; its spatial resolution combined with volumetric capability and the use of injectable contrast medium made CT the working horse in the assessment of neoplastic and inflammatory diseases. In the last two decades, magnetic resonance (MR) supported CT in the most challenging cases, when the optimal contrast resolution due to the multisequence portfolio is needed to assess the neoplastic involvement of laryngeal cartilages, paraglottic space(s), and extra laryngeal spread. The aim of this paper is to give a comprehensive radiological overview of larynx and hypopharynx complex anatomy, combining in vivo images, anatomical sections, and images of ex vivo specimens.

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

OBJECTIVES: To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis. RESULTS: Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSIONS: The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC. CLINICAL RELEVANCE STATEMENT: Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making. KEY POINTS: • Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.

PURPOSE: By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC). METHODS: A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR. RESULTS: There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively. CONCLUSIONS: The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance.

[Clinical analysis of retropharyngeal lymph node metastasis in patients with hypopharyngeal squamous cell carcinoma].

Objective: To analyze the incidence and the related risk factors of retropharyngeal lymph node metastasis in patients with hypopharyngeal squamous cell carcinoma, evaluate the accuracy of preoperative enhanced CT in judging retropharyngeal lymph node metastasis, and investigate the impact of retropharyngeal lymph node metastasis on the prognosis. Methods: Retrospective analyses were made on 398 patients with hypopharyngeal squamous cell carcinoma who underwent surgery as the primary therapy and accepted retropharyngeal lymph node exploration and clearance during surgery in Shandong Provincial ENT Hospital from January 2014 to December 2019. Multivariate logistic regression analysis was used to clarify the related risk factors of retropharyngeal lymph node metastasis. Multivariate Cox regression analysis was used to investigate the impact of retropharyngeal lymph node metastasis on prognosis. The retropharyngeal lymph nodes of 218 cases with available preoperative enhanced CT images were evaluated by two experienced radiologists and compared with postoperative pathological results. Results: Retropharyngeal lymph node metastasis were confirmed in 54 of 398 (13.6%) cases according to postoperative pathology. The sensitivity and specificity of preoperative enhanced CT in the diagnosis of retropharyngeal lymph node metastasis were 34.6% and 91.1%, respectively, and the overall accuracy was 84.4%. Multivariate logistic regression analysis showed that the site of the primary lesion and pathological N stage were independent risk factors for retropharyngeal lymph node metastasis in hypopharyngeal squamous cell carcinoma. Patients with primary lesion located in the posterior wall of hypopharynx (OR=4.83, 95% CI: 1.27-18.40), N2 stage (OR=6.30, 95% CI: 2.25-17.67), and N3 stage (OR=26.89, 95% CI: 5.76-125.58) were prone to retropharyngeal lymph node metastasis. The 5-year overall survival rate of the 398 patients was 50.4%, and the 5-year disease-free survival rate was 48.3%. Multivariate Cox regression analysis showed that T stage, N stage, retropharyngeal lymph node metastasis, and radiotherapy were independent influencing factors for overall survival (T stage: HR=1.28, 95% CI: 1.06-1.54; N stage: HR=1.26, 95% CI: 1.14-1.40; retropharyngeal lymph node metastasis: HR=2.13, 95% CI: 1.47-3.08; radiotherapy: HR=0.54, 95% CI: 0.38-0.76) and disease-free survival of patients with hypopharyngeal squamous cell carcinoma (T stage: HR=1.26, 95% CI: 1.06-1.51; N stage: HR=1.25, 95% CI: 1.13-1.37; retropharyngeal lymph node metastasis: HR=2.24, 95% CI: 1.56-3.21; radiotherapy: HR=0.55, 95% CI: 0.40-0.77). Conclusions: Metastasis of retropharyngeal lymph nodes in hypopharyngeal squamous cell carcinoma is not rare. Enhanced CT is of low accuracy and limited value in diagnosing retropharyngeal lymph node metastasis. Primary lesions located in the posterior wall of the hypopharyngx, N2 stage, and N3 stage are independent high-risk factors for retropharyngeal lymph node metastasis. The prognosis of hypopharyngeal cancer patients with retropharyngeal lymph node metastasis is worse, and active surgical exploration and clearance can effectively reduce the mortality caused by retropharyngeal lymph node metastasis.

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.

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. METHODS: MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC). RESULTS: The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually (p < 0.001). The DSC of DeepLab V3 + model was significantly higher than that of U-Net model (0.77 vs 0.75, p < 0.05), particularly in those small tumor volumes of < 10 cm3 (0.74 vs 0.70, p < 0.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71-0.91) with manual delineation. The radiomics extracted by DeepLab V3 + model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (p < 0.05). CONCLUSION: Both DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3 + had a better performance than U-Net. CLINICAL RELEVANCE STATEMENT: The deep learning model, DeepLab V3 + , exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes. KEY POINTS: • DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images. • DeepLab V3 + model was more accurate than U-Net in automated segmentation, especially on small tumors. • DeepLab V3 + exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.

Linfangioma hipofaríngeo en adulto: presentación de un caso clínico / Hypopharyngeal lymphangioma in adults: Presentation of a case report

Los linfangiomas son malformaciones linfáticas benignas infrecuentes; se manifiestan principalmente en la infancia. Pueden desarrollarse en cualquier zona del cuerpo, más frecuentemente cabeza y cuello, siendo su compromiso faríngeo extremadamente poco común. Dada su clínica inespecífica se requiere un análisis anatomopatológico preciso para concretar un correcto diagnóstico. Presentamos el caso de una mujer de 40 años con historia de disfagia y odinofagia de un año de evolución cuyo estudio imagenológico y con nasofibroscopía evidenció un tumor redondeado alojado en seno piriforme derecho, la biopsia excisional confirmó el diagnóstico de linfangioma. Se debe considerar el diagnóstico de linfangioma dentro de los diagnósticos diferenciales de tumores en faringe y concientizar su presencia en pacientes adulto, para ofrecer el manejo óptimo basado, ya sea en escisión quirúrgica completa, para evitar la recurrencia asociado, o al uso de sustancias esclerosantes.

Lymphangiomas are infrequent benign lymphatic malformations, with clinical manifestations mainly at birth or in childhood. They can develop in any area of the body, most frequently the head and neck, and their pharyngeal involvement is extremely rare. Given its nonspecific symptoms, it requires a precise pathological analysis to make a correct diagnosis. We present the case of a 40-year-old woman with a 1-year history of dysphagia and odynophagia whose imaging study and nasofibroscopy revealed a rounded tumor lodged in the right pyriform sinus. The excisional biopsy confirmed the diagnosis of lymphangioma. The diagnosis of lymphangioma should be considered within the differentials of tumors in the pharynx and awareness of its presence in adults to offer optimal management based on complete surgical excision to avoid recurrence or management with use of sclerosing substances.

Twist-Net: A multi-modality transfer learning network with the hybrid bilateral encoder for hypopharyngeal cancer segmentation.

Hypopharyngeal cancer (HPC) is a rare disease. Therefore, it is a challenge to automatically segment HPC tumors and metastatic lymph nodes (HPC risk areas) from medical images with the small-scale dataset. Combining low-level details and high-level semantics from feature maps in different scales can improve the accuracy of segmentation. Herein, we propose a Multi-Modality Transfer Learning Network with Hybrid Bilateral Encoder (Twist-Net) for Hypopharyngeal Cancer Segmentation. Specifically, we propose a Bilateral Transition (BT) block and a Bilateral Gather (BG) block to twist (fuse) high-level semantic feature maps and low-level detailed feature maps. We design a block with multi-receptive field extraction capabilities, M Block, to capture multi-scale information. To avoid overfitting caused by the small scale of the dataset, we propose a transfer learning method that can transfer priors experience from large computer vision datasets to multi-modality medical imaging datasets. Compared with other methods, our method outperforms other methods on HPC dataset, achieving the highest Dice of 82.98%. Our method is also superior to other methods on two public medical segmentation datasets, i.e., the CHASE_DB1 dataset and BraTS2018 dataset. On these two datasets, the Dice of our method is 79.83% and 84.87%, respectively. The code is available at: https://github.com/zhongqiu1245/TwistNet.

Clinical Management of Early-Stage Hypopharyngeal Squamous Cell Carcinoma: A Single-Institution Clinical Analysis.

OBJECTIVE: Early detection of hypopharyngeal squamous cell carcinoma (SCC) is important for both an improved prognosis and less-invasive treatment. We retrospectively analyzed the detection rates of early hypopharyngeal SCCs according to the evaluation methods and the clinical management of early hypopharyngeal SCCs. METHODS: Sixty-eight patients with early hypopharyngeal SCC who were diagnosed were reviewed. RESULTS: The number of early hypopharyngeal cancer patients with asymptomatic or synchronous or metachronous esophageal cancer examined by upper gastrointestinal endoscopy with narrow-band imaging (NBI) was significantly higher than those examined by laryngopharyngeal endoscopy with NBI. The 3-year disease-specific survival rates according to T classification were as follows: Tis, 100%; T1, 100%; T2, 79.8%; and overall, 91.2%, respectively. CONCLUSIONS: Early-stage hypopharyngeal SCC can be cured by minimally invasive transoral surgery or radiotherapy. Observation of the pharynx using NBI in patients with a history of head and neck cancer, esophageal cancer, gastric cancer, or pharyngeal discomfort is very important, and routinely examining the pharynx with NBI, even in patients undergoing endoscopy for screening purposes, is recommended.

Evaluating hypopharyngeal carcinoma using narrow band imaging and oxygen-injected laryngoscope: New technique.

OBJECTIVE: To evaluate the diagnostic value of narrow band imaging (NBI) endoscopic classification for hypopharyngeal lesions and to lay the groundwork for practical applications of oxygen-injected laryngoscope for hypopharyngeal carcinoma (HC). METHODS: A total of 140 subjects with suspected 146 hypopharyngeal lesions were selected for pathological examination. Subsequently, NBI and white light imaging (WLI) endoscopy were performed to observe and classify lesions into 7 types according to our modified NBI classification. Pathological results were used as the gold standard to assess the diagnostic value of the NBI classification. The value of oxygen-injected laryngoscope for accurate assessment of lesion extension was evaluated based on the exposure of hypopharyngeal lesions before and after use. RESULTS: The accuracy, sensitivity, and negative predictive value of NBI endoscopy in diagnosing hypopharyngeal lesions were 95.9 %, 96.7 %, and 84.6 %, respectively, which were higher than those of WLI mode (p < 0.05). NBI endoscopy was more accurate than WLI in diagnosing malignant lesions (p < 0.05), especially for high-grade dysplasia (HGD) (p < 0.05). There was remarkable consistency between NBI classification and pathological results (Kappa = 0.855). Type Va and type Vb-c accounted for 72.7 % and 92.8 % of HGD and invasive carcinoma, respectively. Moreover, the oxygen-injected laryngoscope was found to provide a more accurate assessment of HC extension (P < 0.001). CONCLUSION: We propose a more appropriate NBI endoscopic classification for hypopharyngeal lesions, which can effectively improve diagnostic accuracy, especially for the early diagnosis of hypopharyngeal cancer. Moreover, the application of oxygen-injected laryngoscope is essential for the accurate assessment of HC and has a high clinical utility.

Dual-energy CT iodine map in predicting the efficacy of neoadjuvant chemotherapy for hypopharyngeal carcinoma: a preliminary study.

Neoadjuvant chemotherapy has become one of the important means for advanced hypopharyngeal carcinoma. So far, there is no effective index to predict the curative effect. To investigate the value of iodine map of dual-energy computed tomography (CT) in predicting the efficacy of neoadjuvant chemotherapy for hypopharyngeal carcinoma. A total of 54 hypopharyngeal carcinomapatients who underwent two courses of TPF neoadjuvant chemotherapy were recruited in this study. Three cases had a complete response (CR), thirty-six cases had a partial response (PR), eleven cases had stable disease (SD), and four cases had a progressive disease (PD) after the chemotherapy. All patients underwent a dual-source CT scan before chemotherapy and rescanned after chemotherapy. The normalized iodine-related attenuation (NIRA) of the mean of maximum slice and most enhanced region of lesion at arterial and parenchymal phase were measured: NIRAmean-A, NIRAmax-A, NIRAmean-P, and NIRAmax-P, respectively. Correlation analysis was conducted between different metrics of NIRA and the diameter change rate of lesions, and the curative effect was evaluated based on the receiver operating characteristic (ROC) curve. There were a significant correlation between NIRAmean-A, NIRAmax-A, NIRAmean-P, NIRAmax-P and the change rate of lesion's maximum diameter (ΔD%) (all P < 0.01). The NIRAmax-A, NIRAmean-P, NIRAmax-P had significant differences between CR, PR, SD, PD groups, but NIRAmean-A did not reach a significant difference. All NIRAmean-A, NIRAmax-A, NIRAmean-P, NIRAmax-P had significant differences between effective (CR + PR) and ineffective (SD + PD) groups. The ROC analysis revealed that NIRAmean-P had the largest AUC and prediction efficacy (AUC = 0.809). Dual-energy CT iodine map could predict the efficacy of neoadjuvant chemotherapy and provides imaging evidence to assist in treatment decisions for hypopharyngeal carcinoma patients.

PET/Computed Tomography: Laryngeal and Hypopharyngeal Cancers.

Treatment of laryngeal and hypopharyngeal tumors with surgery, radiation therapy, and chemotherapy is aimed at improving survival and preserving function. PET with fluorodeoxyglucose F 18 (18F FDG-PET)/computed tomography is the standard of care and an integral part of staging and treatment response assessment in patients with laryngeal and hypopharyngeal cancers. Knowledge of cross-sectional laryngeal and hypopharyngeal anatomy, expected patterns of tumor spread, and awareness of physiologic FDG uptake in head and neck structures is essential for accurate TNM staging. 18F FDG-PET/computed tomography is superior to anatomic imaging in identifying posttreatment local, regional, and distant tumor recurrence.

Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study.

OBJECTIVES: This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy. METHODS: Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables. RESULTS: The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWIintra). The log-rank test showed that DWIintra was significantly associated with PFS (p = 0.013). DWIintra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023). CONCLUSION: DL models using DWIintra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment. KEY POINTS: • Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.

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

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