How Should We Approach Locally Advanced Squamous Cell Carcinoma of Head and Neck Cancer Patients Ineligible for Standard Non-surgical Treatment?

Multidisciplinary tumor board for head and neck cancer from the perspective of medical oncologists-optimizing its effectiveness

Head and neck cancer (HNC) treatment is becoming increasingly multidisciplinary, and patient characteristics vary. Therefore, a multidisciplinary tumor board (MTB) is essential in clinical practice. This review provides insights into the benefits and tips for improving head and neck MTB from the perspective of medical oncologists. The MTB is a platform to discuss the optimal application of the standard of care to each case, reach a consensus, and establish a recommendation to support patients' decision-making. A productive and educational MTB also provides an opportunity to share information on ongoing clinical trials with physicians. Case presentations should be systematic to discuss all new and challenging cases before, during, and after the treatment. Human resource development, particularly of head and neck medical oncologists, is crucial. The type of multidisciplinary network between medical staff and the extent of patient intervention differs among MTB teams. Subsequently, a virtual MTB can establish a medical network between institutions that will contribute to the equalization and centralization of head and neck oncologic care.

Management of Older Patients with Head and Neck Cancer: A Comprehensive Review

The projected increase in life expectancy over the next few decades is expected to result in a rise in age-related diseases, including cancer. Head and neck cancer (HNC) is a worldwide health problem with high rates of morbidity and mortality. In this report, we have critically reviewed the literature reporting the management of older patients with HNC. Older adults are more prone to complications and toxicities secondary to HNC treatment, especially those patients who are frail or have comorbidities. Thus, this population should be screened prior to treatment for such predispositions to maximize medical management of comorbidities. Chronologic age itself is not a reason for choosing less intensive treatment for older HNC patients. Whenever possible, also older patients should be treated according to the best standard of care, as nonstandard approaches may result in increased treatment failure rates and mortality. The treatment plan is best established by a multidisciplinary tumor board with shared decision-making with patients and family. Treatment modifications should be considered for those patients who have severe comorbidities, evidence of frailty (low performance status), or low performance status or those who refuse the recommendations of the tumor board.

Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review

Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.

Early-stage vocal cord cancer treated with hypofractionated radiotherapy to the larynx with or without concurrent chemotherapy

BACKGROUND

We report outcomes among patients with T2 and select T3 glottic squamous cell carcinoma (SCC) treated with radiotherapy.

METHODS

We reviewed T2 and T3 (only paraglottic space invasion) N0 M0 glottic SCC patients treated with curative-intent hypofractionated larynx radiotherapy, with or without concurrent systemic therapy.

RESULTS

Of 71 patients, those who received concurrent chemotherapy (23/71; 32%) had worse prognostic factors, including impaired cord mobility (70% vs. 40%, p = 0.02) and larger median gross tumor volume (3.0 vs. 1.6 cm³ , p = 0.003). Over a median follow-up of 3.8 years, 2-year local control among patients with impaired cord mobility appeared higher for those who received chemotherapy (88% vs. 61%, p = 0.12), but the difference was not statistically significant. Acute and late toxicity rates were not higher among patients who received chemotherapy.

CONCLUSIONS

The addition of concurrent platinum-based chemotherapy to hypofractionated larynx radiotherapy among patients with early-stage glottic SCC with impaired cord mobility appears safe and worthy of additional investigation.

Paclitaxel Plus Cetuximab as Induction Chemotherapy for Patients With Locoregionally Advanced Head and Neck Squamous Cell Carcinoma Unfit for Cisplatin-Based Chemotherapy

Objectives

Induction chemotherapy (ICT) followed by definitive treatment is an accepted non-surgical approach for locoregionally advanced head and neck squamous cell carcinoma (LA-HNSCC). However, ICT remains a challenge for cisplatin-unfit patients. We evaluated paclitaxel and cetuximab (P-C) as ICT in a cohort of LA-HNSCC patients unfit for cisplatin.

Materials and Methods

This is a retrospective analysis of patients with newly diagnosed LA-HNSCC considered unfit for cisplatin-based chemotherapy (age >70 and/or ECOG≥2 and/or comorbidities) treated with weekly P-C followed by definitive radiotherapy and cetuximab (RT-C) between 2010 and 2017. Toxicity and objective response rate (ORR) to ICT and RT-C were collected. Median overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method. Cox regression analysis was performed to determine baseline predictors of OS and PFS.

Results

A total of 57 patients were included. Grade 3–4 toxicity rate to ICT was 54.4%, and there was a death deemed treatment-related (G5). P-C achieved an ORR of 66.7%, including 12.3% of complete responses (CR). After P-C, 45 patients (78.9%) continued with concomitant RT-C. Twenty-six patients (45.6%) achieved a CR after definitive treatment. With a median follow-up of 21.7 months (range 1.2–94.6), median OS and PFS were 22.9 months and 10.7 months, respectively. The estimated 2-year OS and PFS rates were 48.9% and 33.7%, respectively. Disease stage had a negative impact on OS (stage IVb vs. III–IVa: HR = 2.55 [1.08–6.04], p = 0.03), with a trend towards worse PFS (HR = 1.92 [0.91–4.05], p = 0.09). Primary tumor in the larynx was associated with improved PFS but not OS (HR = 0.45 [0.22–0.92], p = 0.03, and HR = 0.69 [0.32–1.54], p = 0.37, respectively).

Conclusion

P-C was a well-tolerated and active ICT regimen in this cohort of LA-HNSCC patients unfit for cisplatin-based chemotherapy. P-C might represent a valid ICT option for unfit patients and may aid patient selection for definitive treatment.

Texture analysis imaging "what a clinical radiologist needs to know"

Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.

Application of radiomics and machine learning in head and neck cancers

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