The most common chemoradiotherapy regimen is high-dose (100 mg/m²) three-weekly cisplatin with concomitant radiotherapy; however, this protocol is associated with acute and late toxicities. Here, we reviewed the dose intensity and toxicity for concomitant weekly cisplatin and radiotherapy in patients with head and neck cancer. Fifty-three patients with untreated head and neck cancer were enrolled and evaluated at our institution from April 2006 to April 2010. Weekly cisplatin (40 mg/m²) was given on weeks 1, 2, 3, 5, 6 and 7 with radiotherapy, which comprised a standard dose of 70 Gy delivered in 35 daily fractions over 7 weeks. Fifty-one patients (96.2%) received the full dose of radiotherapy, while the course was disrupted by adverse events in two. Over the course of the chemotherapy, 31 patients (58.5%) received more than 200 mg/m² cisplatin. The toxicity was manageable in all except one patient, who died of sepsis after completing treatment. The 2-year overall survival rate and local progression-free rate for all patients were 93.7% and 88.0%, respectively. The primary site showed a complete response in 52 patients (98.1%) and a partial response in 1 patient (1.9%). The primary disease was well controlled by chemoradiotherapy in 47 patients (88.7%). Weekly cisplatin could be easier to manage than three-weekly cisplatin, because patients can be monitored more regularly for toxicity allowing the schedule to be altered if required. This regimen appears to be a suitable alternative to three-weekly high-dose cisplatin with concomitant radiotherapy.

The purpose of this study was to evaluate the efficacy of superselective cisplatin infusion with concomitant radiotherapy (RADPLAT) for previously untreated patients with the squamous cell carcinoma of maxillary sinus (SCC-MS). Between 1999 and 2010, 54 patients were given superselective intra-arterial infusions of cisplatin (100–120 mg m⁻² per week) with simultaneous intra-venous infusions of thiosulfate to neutralise cisplatin toxicity and conventional radiotherapy (65–70 Gy). One patient (1.9%) was diagnosed with T2, 14 (25.9%) with T3, 27 (50%) with T4a, and 12 (22.2%) with T4b disease. Lymph-node involvement was present in 12 patients (22.2%). During the median follow-up period of 6.4 years, the 5-year local progression-free and overall survival rates were 65.8 and 67.9% for all patients, respectively. No patient died as a result of treatment toxicity or experienced a cerebrovascular accident. Osteonecrosis (n=5), brain necrosis (n=1), and ocular/visual problems (n=14) were observed as late adverse reactions. We have shown excellent overall survival and local progression-free rate in SCC-MS patients treated by RADPLAT with acceptable rates of acute and late toxicity. A multi-institutional trial is needed to prove that this strategy is a feasible and effective approach for the treatment of SCC-MS.

To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML). The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI). The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10− 1 vs. 2.53 ± 0.12 × 10− 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10− 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10− 1). Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Background: To fill the data gap between clinical trials and real-world settings, this study assessed the overall effectiveness and safety of nivolumab in patients with head and neck cancer (HNC) during Japanese real-world clinical practice. Methods: This was a multicenter, retrospective study in Japanese patients with recurrent or metastatic HNC who received nivolumab for the first time between July and December 2017. Data on the clinical use, effectiveness, and safety of nivolumab were extracted from patient medical records. Results: Overall, 256 patients were enrolled in this study. The median duration of nivolumab treatment was 72.5 days, with patients receiving a median of 6.0 (range 1–27) doses. Median overall survival (OS) was 9.5 (95% confidence interval [CI] 8.2–12.0) months and the estimated 12-month OS rate was 43.2%. The objective response rate (ORR) was 15.7% overall and 21.1%, 7.1%, and 13.6% in patients with primary nasopharynx, maxillary sinus, and salivary gland tumors, respectively, who had been excluded from CheckMate 141. Grade ≥ 3 immune-related adverse events occurred in 5.9% of patients. No new safety signals were identified compared with adverse events noted in CheckMate 141. Conclusions: The effectiveness and safety of nivolumab in real-world clinical practice are consistent with data from the CheckMate 141 clinical trial. Therapeutic response was also observed in the groups of patients excluded from CheckMate 141. Trial registration number: UMIN-CTR (UMIN000032600), Clinicaltrials.gov (NCT03569436)

Exosomes are a new way of the communication between the tumor cell and macrophage in the micro-environment. The macrophage can be induced to different phenotypes according to the different tumors. In the present study, long-chain noncoding RNA HOTAIR (lncRNA HOTAIR) was highly expressed in LSCC and exosomes. The pathway of exosomal lncRNA HOTAIR inducing macrophage to M2 polarization in the LSCC was investigated. The carcinoma tissues and adjacent tissues were collected from 104 LSCC cases, and the positive relationship between CD163-/CD206-M2 macrophage infiltration and clinical phase, lymph node spreading and pathological phase in LSCC was observed. To examine the role of exosomal lncRNA HOTAIR, macrophages were co-cultured with LSCC-exosomes of high lncRNA HOTAIR expression or transferred with HOTAIR mimics. It was suggested that exosomal lncRNA HOTAIR can induce macrophages to M2 polarization by PI3K/p-AKT/AKT signaling pathway. Furthermore, exo-treated M2 macrophages facilitate the migration, proliferation, and EMT of LSCC.

The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.