Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer.

BACKGROUND: Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). PURPOSE: To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. STUDY TYPE: Prospective. POPULATION: A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. ASSESSMENT: Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. STATISTICAL TESTS: Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. RESULTS: Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). DATA CONCLUSION: We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Outcomes of mandibular reconstruction using three-dimensional custom-made porous titanium prostheses.

BACKGROUND: Our aim was to report the long-term outcomes of mandibular reconstruction using CAD-CAM-designed 3D-printed porous titanium implants in patients not amenable to a free vascularized fibula flap reconstruction. METHODS: The implants were designed with ProPlan CMF® 2.2 software and manufactured with a Selective Laser Melting (SLM) "layer-by-layer" 3D-printing of pure porous titanium powder beds. Primary endpoints were implant exposure and implant removal calculated using Gray's tests. Secondary endpoints were predictive factors of implant exposure and implant removal, and rates of dental rehabilitation. RESULTS: Thirty-six patients were operated between 2015 and 2017 and were included in this study. Reconstruction using a porous titanium 3D-printed implant was proposed due to medical contraindication for a fibula free flap (n = 13), due to the failure of a previous fibula free flap reconstruction (n = 7), or due to refusal of a fibula free flap reconstruction by the patient (n = 16). The medical indications for mandibular reconstruction were a primary tumor requiring mandibulectomy in nine patients, mandibular osteoradionecrosis requiring mandibulectomy in nineteen patients, and secondary reconstruction in eight patients. The 2-year rates of implant exposure and implant removal were 69.4% and 52.8%. Reconstruction of the symphysis was a high-risk exposure variable (OR 30; p = 0.0003). Only one patient underwent a successful dental rehabilitation. CONCLUSION: The use of a porous titanium 3D- implant for mandibular reconstruction in head and neck cancer patients resulted in high rates of implant exposure and of implant removal, notably when symphysis involvement.

An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data.

BACKGROUND: The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a prognostic model for solid tumour patients using AI on multimodal data. PATIENTS AND METHODS: Our retrospective study included examinations of patients with seven different cancer types performed between 2003 and 2017 in 17 different hospitals. Radiologists annotated all metastases on baseline computed tomography (CT) and ultrasound (US) images. Imaging features were extracted using AI models and used along with the patients' and treatments' metadata. A Cox regression was fitted to predict prognosis. Performance was assessed on a left-out test set with 1000 bootstraps. RESULTS: The model was built on 436 patients and tested on 196 patients (mean age 59, IQR: 51-6, 411 men out of 616 patients). On the whole, 1147 US images were annotated with lesions delineation, and 632 thorax-abdomen-pelvis CTs (total of 301,975 slices) were fully annotated with a total of 9516 lesions. The developed model reaches an average concordance index of 0.71 (0.67-0.76, 95% CI). Using the median predicted risk as a threshold value, the model is able to significantly (log-rank test P value < 0.001) isolate high-risk patients from low-risk patients (respective median OS of 11 and 31 months) with a hazard ratio of 3.5 (2.4-5.2, 95% CI). CONCLUSION: AI was able to extract prognostic features from imaging data, and along with clinical data, allows an accurate stratification of patients' prognoses.

Weight and skeletal muscle loss with cabozantinib in metastatic renal cell carcinoma.

BACKGROUND: Cabozantinib, a standard of care metastatic renal cell carcinoma (mRCC), may be associated with weight and muscle loss. These effects of new generation VEGFR tyrosine kinase inhibitor on muscle mass loss are poorly described. METHODS: All cabozantinib-treated mRCC patients from January 2014 to February 2019 in our institution were included. Clinical data including weight were collected during therapy. Computed tomography images were centrally reviewed for response assessment, and axial sections at the third lumbar vertebrae were used to measure the total muscle area. Toxicities and cabozantinib outcomes were evaluated. Co-primary endpoints included skeletal muscle loss and weight loss (WL), longitudinally evaluated during treatment. WL has been classified according to CTCAEv5.0: Grade 1 (loss of 5 to <10% of baseline body weight), Grade 2 (loss of 10% to <20% of baseline body weight), and Grades 3-4 (loss >20% of baseline body weight). RESULTS: Patients were mostly men (70.3%), median age was 59.2 (range: 22.0-78.0) years, and median baseline body mass index was 25.0 (range: 16.4-49.3) kg/cm2 . Prognosis according to International Metastatic RCC Database Consortium score was good, intermediate, and poor for 13 (13.0%), 63 (63.0%), and 24 (24.0%) patients, respectively. Out of a total of 120 patients, 101 patients with a median follow-up of 22.3 months (range: 4.5-62.2) were eligible for analysis; 85 experienced muscle loss and muscle loss >10% increased during cabozantinib exposition, especially after 6 months of treatment. At cabozantinib baseline, 71 patients (70.3%) had sarcopenia, and 16/30 (53.3%) non-sarcopenic patients developed sarcopenia during treatment. Baseline sarcopenia was associated with lower response rates (P = 0.031) and higher grades 3-4 toxicities (P = 0.001). Out of 92 patients included in the WL analysis, 44 (47.8%) and 12 (13.0%) experienced grades 2 and 3 WL, respectively. CONCLUSIONS: We report a high incidence of grades 3-4 WL, fourth times higher than reported in prior pivotal trials, and half of the patients developed sarcopenia while on cabozantinib treatment. Weight and muscle mass loss with cabozantinib are underreported and may require further investigations and early management.

Orofacial intramuscular lymphoma: first presentation.

The term lymphoma includes a wide variety of different clinical entities including diffuse large B-cell lymphomas (DLBCL). Skeletal muscle or intramuscular lymphomas represent less than 2% of B-cell Lymphoma, they are quite rare, even more in the orofacial area. We present the case of a painless growing mass of the right cheek mimicking a chronic oral cellulitis in a 34-year-old man. Magnetic resonance imaging (MRI) of the mandible revealed a well-defined 7x3cm mass around the core of the mandible that invades the buccal floor and the subcutaneous planes. A whole-body 18F-FDG PET/CT for the initial diagnosis revealed an intensely isolated hypermetabolic band corresponding to a voluminous tumoral permeation. The diagnosis of a skeletal muscle diffuse large B-cell lymphoma was established after an intraoral biopsy. It was treated with 4 chemotherapy cures and showed complete remission at one year of follow-up. This atypical form of lymphoma should be integrated into the differential diagnosis of soft tissue tumors in the oral cavity.

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.

Head and neck Ewing sarcoma: French surgical practice analysis pleads for surgery centralization.

This study aimed to analyze surgical procedures for head and neck Ewing sarcoma (HNES) with regard to oncological, functional, and esthetic outcomes. A blinded multidisciplinary retrospective chart review of operated French HNES patients (Euro-EWING 99 trial, 1999-2014) was performed to assess patient/tumor characteristics, treatment details, and outcomes. Primary surgery without reconstruction was undertaken in 13 patients (emergency context/misdiagnosis). However, because of contaminated surgical margins, all patients had to undergo systematic postoperative radiotherapy. Twenty-six patients underwent multidisciplinary evaluation and were scheduled to undergo postchemotherapy surgery, with 19 patients scheduled for immediate reconstruction. All cases showed R0 margins after postchemotherapy surgery of the initial tumor bed by multidisciplinary surgical teams, while n = 3/4 of local relapses (very poor prognosis) had R1a margins after surgery of the residual tumor volume following chemotherapy. Only three surgical expertise centers operated on ≥ 4 patients over the 15-year period. Thirty patients developed long-term sequelae, with increased complications following radiotherapy. Referring patients to surgical expertise centers following a suspected diagnosis, with planned postchemotherapy surgery of the initial tumor bed at these centers, might limit the need for intralesional resections, allowing radical R0 resections and thus reducing long-term sequelae as well as the risk of secondary radio-induced malignancy by limiting the need for postoperative radiotherapy.

Can Deep Learning Replace Gadolinium in Neuro-Oncology?: A Reader Study.

MATERIALS AND METHODS: This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 patients were included: 107 formed the training sample (55 ± 14 years, 58 women) and 38 the separate test sample (62 ± 12 years, 22 women). Patients had glioma, brain metastases, meningioma, or no enhancing lesion. T1, T2-FLAIR, diffusion-weighted imaging, low-dose, and standard-dose postcontrast T1 sequences were acquired. A deep network was trained to process the precontrast and low-dose sequences to predict "virtual" surrogate images for contrast-enhanced T1. Once trained, the deep learning method was evaluated on the test sample. The discrepancies between the predicted virtual images and the standard-dose MRIs were qualitatively and quantitatively evaluated using both automated voxel-wise metrics and a reader study, where 2 radiologists graded image qualities and marked all visible enhancing lesions. RESULTS: The automated analysis of the test brain MRIs computed a structural similarity index of 87.1% ± 4.8% between the predicted virtual sequences and the reference contrast-enhanced T1 MRIs, a peak signal-to-noise ratio of 31.6 ± 2.0 dB, and an area under the curve of 96.4% ± 3.1%. At Youden's operating point, the voxel-wise sensitivity (SE) and specificity were 96.4% and 94.8%, respectively. The reader study found that virtual images were preferred to standard-dose MRI in terms of image quality (P = 0.008). A total of 91 reference lesions were identified in the 38 test T1 sequences enhanced with full dose of contrast agent. On average across readers, the brain lesion SE of the virtual images was 83% for lesions larger than 10 mm (n = 42), and the associated false detection rate was 0.08 lesion/patient. The corresponding positive predictive value of detected lesions was 92%, and the F1 score was 88%. Lesion detection performance, however, dropped when smaller lesions were included: average SE was 67% for lesions larger than 5 mm (n = 74), and 56% with all lesions included regardless of their size. The false detection rate remained below 0.50 lesion/patient in all cases, and the positive predictive value remained above 73%. The composite F1 score was 63% at worst. CONCLUSIONS: The proposed deep learning method for virtual contrast-enhanced T1 brain MRI prediction showed very high quantitative performance when evaluated with standard voxel-wise metrics. The reader study demonstrated that, for lesions larger than 10 mm, good detection performance could be maintained despite a 4-fold division in contrast agent usage, unveiling a promising avenue for reducing the gadolinium exposure of returning patients. Small lesions proved, however, difficult to handle for the deep network, showing that full-dose injections remain essential for accurate first-line diagnosis in neuro-oncology.

18F-FDG PET/CT for early response assessment of jaw osteoradionecrosis after the PENTOCLO protocol: A promising imaging modality.

INTRODUCTION: The Pentoxifylline, Tocopherol and Clodronate protocol (PENTOCLO) showed promising results for jaw osteoradionecrosis (ORN) management. However, the clinical and radiological improvements are often delayed, leading to unwanted long-term treatment, with potential loss of opportunity for more radical surgical treatments. Our objective was to assess the diagnosis performance of 18F-FDG PET/CT to early predict ORN response to the PENTOCLO protocol. MATERIALS AND METHODS: All patients from our center who were treated with the PENTOCLO protocol and with a 18F-FDG PET/CT performed at diagnosis and three months after the end of antibiotherapy were retrospectively included. The PENTOCLO protocol was always combined with prior appropriate antibiotherapy for six weeks. The healing endpoint was divided into healing, stability or worsening, according to the combination of clinical and radiological assessments at the date of last follow-up. For each patient, the difference between the maximal standardized uptake value (ΔSUVmax) of the ORN lesion at three months and baseline were computed. Diagnostic performance of 18F-FDG PET/CT was evaluated by sensitivity, specificity and the area under the receiver operating characteristic curve (ROC-AUC) of ΔSUVmax. RESULTS: 24 patients were included with an average follow-up of 29.3 months. The healing, stability and worsening rate were 25%, 62.5% and 12.5% respectively. The AUC for discriminating worsening vs stability or healing was 0.92 (IC95 [0.81-1.00]). A ΔSUVmax greater than or equal to 0 was predictive of a worsening with a sensitivity and specificity of 84 and 66% respectively. CONCLUSION: 18F-FDG PET/CT imaging could be useful for early prediction of PENTOCLO treatment resistance with appropriate antibiotherapy.

Imaging of Skull Base and Orbital Invasion in Sinonasal Cancer: Correlation with Histopathology.

BACKGROUND: Pretreatment assessment of local extension in sinonasal cancer is essential for prognostic evaluation and surgical planning. The aim of this study was to assess the diagnostic performance of two common imaging techniques (CT and MRI) for the diagnosis of skull base and orbital invasion by comparing imaging findings to histopathological data. METHODS: This was a retrospective two-center study including patients with sinonasal cancer involving the skull base and/or the orbit operated on between 2000 and 2019. Patients were included only if pre-operative CT and/or MRI, operative and histopathologic reports were available. A double prospective blinded imaging review was conducted according to predefined radiological parameters. Radiologic tumor extension was compared to histopathological reports, which were considered the gold standard. The predictive positive value (PPV) for the diagnosis of skull base/orbital invasion was calculated for each parameter. RESULTS: A total of 176 patients were included. Ethmoidal intestinal-type adenocarcinoma was the most common type of cancer (41%). The PPV for major modification of the bony skull base was 78% on the CT scan, and 89% on MRI. MRI signs of dural invasion with the highest PPVs were: contact angle over 45° between tumor and dura (86%), irregular deformation of dura adjacent to tumor (87%) and nodular dural enhancement over 2 mm in thickness (87%). Signs of orbital invasion had low PPVs (<50%). CONCLUSIONS: This retrospective study provides objective data about the diagnostic value of pretreatment imaging in patients with sinonasal cancer.

Three artificial intelligence data challenges based on CT and ultrasound.

PURPOSE: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers. MATERIALS AND METHODS: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams. The objectives were proposed by the different organizations depending on their core areas of expertise. A dedicated platform was used to upload the medical image data, to automatically anonymize the uploaded data. RESULTS: Three challenges were proposed including classification of benign or malignant breast nodules on ultrasound examinations, detection and contouring of pathological neck lymph nodes from cervical CT examinations and classification of calcium score on coronary calcifications from thoracic CT examinations. A total of 2076 medical examinations were included in the database for the three challenges, in three months, by 18 different centers, of which 12% were excluded. The 39 participants were divided into six multidisciplinary teams among which the coronary calcification score challenge was solved with a concordance index > 95%, and the other two with scores of 67% (breast nodule classification) and 63% (neck lymph node calcifications).

Automatic cervical lymphadenopathy segmentation from CT data using deep learning.

PURPOSE: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination. MATERIALS AND METHODS: An ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies. On examinations without adenopathies, the score was given by the formula M/(M+A) where M was the mean adenopathy volume per patient and A the volume segmented by the algorithm. The networks were trained on 117 annotated CT acquisitions. RESULTS: The test set included 150 additional CT acquisitions unseen during the training. The performance on the test set yielded a mean score of 0.63. CONCLUSION: Despite limited available data and partial annotations, our CNN based approach achieved promising results in the task of cervical lymphadenopathy segmentation. It has the potential to bring precise quantification to the clinical workflow and to assist the clinician in the detection task.

Prognostic value and therapeutic implications of nodal involvement in head and neck mucosal melanoma.

BACKGROUND: The prognostic significance of nodal involvement is not well established in head and neck mucosal melanoma (HNMM). METHODS: A retrospective, monocentric study was performed on 96 patients with HNMM treated between 2000 and 2017. RESULTS: At diagnosis, seventeen patients (17.8%) were cN1, with a higher risk for HNMM arising from the oral cavity (p = 0.01). cN status had no prognostic value in patients with nonmetastatic resectable HNMM. No occult nodal metastasis was observed in the cN0 patients after a nodal dissection (ND). The nodal recurrence rate was similar in the cN1 and the cN0 patients. No isolated nodal recurrences were noted. Among the patients who underwent a ND, no benefit of this procedure was noted. CONCLUSIONS: cN1 status is not a prognostic factor in patients with resectable HNMM. Elective ND should not be systematically performed in cN0 HNMM.

Outcomes in N3 Head and Neck Squamous Cell Carcinoma and Role of Upfront Neck Dissection.

OBJECTIVES: We investigated the prognostic factor of N3 head and neck squamous cell carcinoma (HNSCC), including the role of upfront neck dissection (UFND) before radiotherapy (RT). METHODS: We retrospectively reviewed the charts of consecutive N3 HNSCC patients treated with curative intent RT. RESULTS: In the study, 323 N3 HNSCC patients were included. Of those, 125 patients (39%) had UFND. Median follow-up was 3.9 years (0-14.8 years). Overall survival (OS) at 5 years was 31.2%, and progression-free survival (PFS) was 26%. In the multivariate analysis, OS was improved in PS 0, T1-2 tumors, patients receiving concurrent chemotherapy, never or former smokers, and UFND. UFND was strongly associated with increased OS (45.7% vs. 21.2%, P < .001), and PFS (P < .001). Regardless of neck node size, UFND improved survival (P = .001 for ≤ 7 cm and P = .004 for > 7 cm). CONCLUSION: UFND could improve treatment outcomes in N3 HNSCC, especially for non-oropharyngeal cancer, regardless of neck node size. LEVEL OF EVIDENCE: 2B Laryngoscope, 131:E844-E850, 2021.

CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review.

Texture analysis in medical imaging is a promising tool that is designed to improve the characterization of abnormal images from patients, to ultimately serve as a predictive or prognostic biomarker. However, the nature of image acquisition itself implies variability in each pixel/voxel value that could jeopardize the usefulness of texture analysis in the medical field. In this review, a search was performed to identify current published data for computed tomography (CT) texture reproducibility and variability. On the basis of this analysis, the critical steps were identified with a view of using texture analysis as a reliable tool in medical imaging. The need to specify the CT scanners used and the associated parameters in published studies is highlighted. Harmonizing acquisition parameters between studies is a crucial step for future texture analysis.

Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging, an In Vitro and In Vivo Study.

BACKGROUND: The development and clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for the identification of parameters altering radiomics reproducibility. The aim of this study was to assess the impact of magnetic field strength on magnetic resonance imaging (MRI) radiomics features in neuroradiology clinical practice. METHODS: T1 3D SPGR sequence was acquired on two phantoms and 10 healthy volunteers with two clinical MR devices from the same manufacturer using two different magnetic fields (1.5 and 3T). Phantoms varied in terms of gadolinium concentrations and textural heterogeneity. 27 regions of interest were segmented (phantom: 21, volunteers: 6) using the LIFEX software. 34 features were analyzed. RESULTS: In the phantom dataset, 10 (67%) out of 15 radiomics features were significantly different when measured at 1.5T or 3T (student's t-test, p < 0.05). Gray levels resampling, and pixel size also influence part of texture features. These findings were validated in healthy volunteers. CONCLUSIONS: According to daily used protocols for clinical examinations, radiomic features extracted on 1.5T should not be used interchangeably with 3T when evaluating texture features. Such confounding factor should be adjusted when adapting the results of a study to a different platform, or when designing a multicentric trial.

[Sentinel lymph node biopsy in head and neck oncology]. / Place et modalités des indications de recherche du ganglion sentinelle dans les tumeurs de la tête et du cou.

Sentinel lymph node biopsy (SLNB) has been initially developed for melanoma and breast cancers. Its application in head and neck cancers is recent, probably due to the complexity of the lymphatic drainage, the proximity between the primary tumor and the lymph nodes and the critical anatomical structures (such as the facial nerve). In onco-dermatology, SLNB is validated in head and neck surgery for melanoma with Breslow thickness up to 1mm or ulceration, Merkel carcinoma and high-risk squamous cell carcinoma. Considering the malignancies of the upper aerodigestive tract, the feasibility and oncologic safety of SLNB are now established for T1-T2N0 oral and oropharyngeal squamous cell carcinomas. Thus, it could allow patients with negative sentinel nodes to avoid an unnecessary neck dissection, leading to a decrease of morbidity with an quality of life improvement. For some primary locations (e.g., anterior floor of the mouth) with high proximity between tumor and lymph nodes, it is recommended to remove the tumor before the SLNB so as to improve the detection. New techniques of detection are currently being developed with intra-operative procedures and new tracers (such as tilmanocept), leading to a better accuracy of detection and, probably, new indications.

Anemia and neutrophil-to-lymphocyte ratio in laryngeal cancer treated with induction chemotherapy.

OBJECTIVES/HYPOTHESIS: We studied the influence of the neutrophil-to-lymphocyte ratio (NLR) and anemia on the response to induction chemotherapy (IC) and survival outcomes in laryngeal cancer patients treated with a preservation protocol. STUDY DESIGN: Retrospective single-center case series. METHODS: We analyzed patients with T3 laryngeal cancer treated with IC using a preservation protocol. The NLR and hemoglobin levels were assessed before treatment and after IC. The response to chemotherapy was assessed using Response Evaluation Criteria in Solid Tumours 1.1 and World Heath Organization standards. The oncological endpoints were overall survival (OS) and disease-free survival (DFS). RESULTS: Sixty-eight patients were analyzed. The median NLR and hemoglobin levels before and after IC were 2.76 and 14.5 g/dL, and 2.01 and 11.6 g/dL, respectively. The NLR and anemia before treatment were not correlated, and they were not associated with the response to chemotherapy. However, an NLR > 5 and anemia before treatment were both associated with shorter OS and DFS. Notably, they were the only factors found to be significantly associated with survival outcomes. CONCLUSIONS: In laryngeal cancer, patients treated with a preservation protocol, a high NLR ratio, and anemia before IC were associated with shorter survival, independently of the response to chemotherapy. LEVEL OF EVIDENCE: 4 Laryngoscope, 130:E144-E150, 2020.

Induction chemotherapy followed by radiotherapy for N3 head and neck squamous cell carcinoma.

BACKGROUND: The treatment outcomes for N3 HNSCC treated with induction chemotherapy (ICT) followed by definitive radiation were reported to clarify the role of ICT and potential prognostic factors. METHODS: A retrospective study was conducted on 120 patients with N3 (≥6 cm) HNSCC, who were treated with ICT as initial treatment. Survival outcomes and potential prognostic factors were reported. RESULTS: The response rate to ICT was 68.3%. There was a statistically significant difference between responders and non-responders in terms of 5-year OS (35.1% vs 13.3%, P < .001) and PFS (29.4% vs 7.4%, P < .001). Good response to ICT (P < .001) and upfront neck dissection (UFND) before radiotherapy (P = .016) were factors predicting for better OS. However, UFND before radiotherapy was not associated with improved outcomes among responders. CONCLUSIONS: This study suggests that ICT could be one treatment option for N3 HNSCC. Among responders to ICT, UFND before radiotherapy could be avoided.