Quantitative Evaluation of Head and Neck Cancer Treatment-Related Dysphagia in the Development of a Personalized Treatment Deintensification Paradigm

A Hard Truth to Swallow: Critically Evaluating the MD Anderson Dysphagia Inventory (MDADI) as an Endpoint in Human Papillomavirus-associated Oropharyngeal Cancer Trials

The MD Anderson Dysphagia Inventory (MDADI), a measure of swallowing-related quality of life, has become the preferred patient-reported outcome measure (PROM) in contemporary clinical trials evaluating the experience of human papillomavirus-associated oropharyngeal squamous cell carcinoma (HPVOPSCC) survivors. With many potentially practice-changing studies using the MDADI composite score as either a primary or coprimary endpoint, or as a key secondary endpoint, it is important to understand its psychometric properties as judged by contemporary PROM standards, with a particular focus on its application to contemporary HPVOPSCC populations. In this critical review, we evaluate contemporary HPVOPSCC studies reporting MDADI outcomes, followed by a detailed evaluation of the psychometric properties of the MDADI. Although the focus of this review was the MDADI, the issues discussed are not unique to the MDADI and have broader applicability to the evaluation and assessment of other PROMs currently in use. First, it may be possible to improve administration of the instrument, as related to missing items, scoring, and the number of items required. Second, although in many instances, the MDADI has been intended as a direct or surrogate measure of swallowing physiology, the MDADI composite score captures a broader health-related quality of life construct affected by both swallowing and eating, the latter of which may be affected by a range of nonswallowing treatment-related toxicities. Finally, a clinically meaningful change of 10 in the MDADI composite score, widely accepted and applied to the clinical trial setting, represents an undoubtably clinically relevant difference in unselected head and neck cancer survivors. However, the smallest difference that might be clinically important to a highly functional HPVOPSCC cohort remains uncertain. Understanding the purpose and properties of the MDADI instrument and furthering the sophistication with which we apply it in this population would improve its interpretation in clinical trials.

Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy

PURPOSE

Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients.

MATERIALS AND METHODS

CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared.

RESULTS

CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82).

CONCLUSION

Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.

Radiation-Induced Skin Dermatitis: Treatment With CamWell® Herb to Soothe® Cream in Patients With Head and Neck Cancer Receiving Radiation Therapy

BACKGROUND

Radiation-induced skin dermatitis (RISD) is a common outcome experienced by adult patients with head and neck cancer (HNC) who have undergone radiation therapy. There is no standardized recommended agent for the prevention or management of RISD.

OBJECTIVES

The primary objective of this study was to retrospectively evaluate for effectiveness of a botanical topical agent, CamWell® Herb to Soothe® cream, on RISD.

METHODS

112 patients with HNC undergoing radiation therapy self-reported their RISD topical skin care agent during treatment as standard of care, CamWell used prophylactically, or CamWell use started after the first week of treatment. The primary endpoint was impact of RISD on the patient, as measured by mean Skindex-16 score throughout treatment. Measures were completed weekly.

FINDINGS

The mean Skindex score was statistically significantly lower for the prophylactic group than for the standard-of-care group. CamWell may have played a role in managing RISD when compared to standard-of-care agents.

A Randomized, Double-Blind, Placebo-Controlled, Cross-Over Study to Evaluate the Efficacy of AqualiefTM Mucoadhesive Tablets in Head and Neck Cancer Patients Who Developed Radiation-Induced Xerostomia

Xerostomia, the subjective complaint of dry mouth, is caused by therapeutic interventions or diseases. Nowadays, radiotherapy (RT) in patients with head and neck cancer (HNC) stands out as one of the most important causes of xerostomia. Currently available therapies for the treatment of xerostomia are still less than optimal and xerostomia still represents an unmet clinical need. In this article, we present the results of a prospective clinical study with a new product, Aqualief^TM, in patients treated with curative RT with or without chemotherapy for HNC. Aqualief^TM is based on two main ingredients, carnosine and karkadé, which have acid buffering and antioxidant properties. The study was performed on 30 patients, with 4 of the patients being lost during the study period. Each patient received randomly one of the two treatments, Aqualief^TM or placebo, for 8 days. After a 10-day wash-out period, each patient received the other treatment for a further 8 days. The results show that Aqualief^TM stimulated salivation in these patients and reduced the pH drop that was observed in an equivalent placebo-treated population of patients. Moreover, no serious, treatment-related adverse events were observed. Aqualief^TM has shown positive results, although with limitations due to unsuccessful trial accrual. Therefore, it may be further investigated as a tool for the treatment of RT-related xerostomia.

[The German Sydney Swallow Questionnaire : Reliability and validity in patients with oropharyngeal dysphagia]

BACKGROUND

The Sydney Swallow Questionnaire (SSQ) is a self-report inventory assessing subjective symptoms of oropharyngeal dysphagia with strong content, construct, discriminant, and predictive validity and test-retest reliability in a range of patient populations.

OBJECTIVE

The main aim of this work was to assess the validity and reliability of the German version of the SSQ (SSQ-G).

MATERIALS AND METHODS

In a cross-validation study, 48 adult German-speaking patients (12 women, 36 men) with neurogenic (n = 16), structural (n = 16), and functional (n = 16) oropharyngeal dysphagia were assessed with the SSQ‑G and the MD Anderson Dysphagia Inventory (MDADI). Cronbach's α was applied to assess the reliability. Criteria and construct validity were investigated using the Spearman correlation coefficient.

RESULTS

With Cronbach's α = 0.94, the internal consistency of the SSQ‑G was excellent. The SSQ‑G questions 1 and 17 showed a moderately significant and highly significant correlation coefficient of -0.43 and -0.45, respectively, with MDADI question 1 (p < 0.5, p < 0.001). Between questions 8, 11, and 12 of the SSQ‑G and questions 7, 13, and 10 of the MDADI, coefficients of -0.48 to -0.55 showed a moderate to strong highly significant correlation (p < 0.001). Thus, the reliability and criterion and construct validity were statistically confirmed.

CONCLUSION

The German version of the SSQ (SSQ-G) allows a reliable and valid assessment of functional swallowing difficulties. In combination with questionnaires on symptom-specific quality of life, such as the MDADI, a more differentiated clinical analysis of swallowing problems is thus possible.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Prospective longitudinal patient-reported outcomes of swallowing following intensity modulated proton therapy for oropharyngeal cancer

BACKGROUND AND PURPOSE

With an enlarging population of long-term oropharyngeal cancer survivors, dysphagia is an increasingly important toxicity following oropharynx cancer treatment. While lower doses to normal surrounding structures may be achieved with intensity modulated proton therapy (IMPT) compared to photon-based radiation, the clinical benefit is uncertain.

METHODS AND MATERIALS

Seventy-one patients with stage III/IV oropharyngeal cancer (AJCC 7th edition) undergoing definitive IMPT on a longitudinal prospective cohort study who had completed the MD Anderson Dysphagia Inventory (MDADI) at pre-specified time points were included.

RESULTS

The majority of patients had HPV-positive tumors (85.9%) and received bilateral neck radiation (81.4%) with concurrent systemic therapy (61.8%). Mean composite MDADI scores decreased from 88.2 at baseline to 59.6 at treatment week 6, and then increased to 74.4 by follow up week 10, 77.0 by 6 months follow up, 80.5 by 12 months follow up, and 80.1 by 24 months follow up. At baseline, only 5.6% of patients recording a poor composite score (lower than 60), compared to 61.2% at treatment week 6, 19.1% at follow up week 10, 13.0% at 6 months follow up, 13.5% at 1 year follow up, and 11.1% at 2 years follow up.

CONCLUSIONS

Patient reported outcomes following IMPT for oropharyngeal cancer demonstrates decreased swallowing function at completion of treatment with relatively rapid recovery by 10 weeks follow up and steady improvement through 2 years. The results are comparable to similar longitudinal studies of photon-based radiotherapy for oropharynx cancer, and suggest that IMPT confers no additional excess toxicity related to swallowing.

Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm

PURPOSE

Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.

METHODS

A total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.

RESULTS

All methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).

CONCLUSION

ML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.

Needs and Challenges for Radiation Oncology in the Era of Precision Medicine

Modern medicine, including the care of the cancer patient, has significantly advanced, with the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now also recognize the tremendous heterogeneity not only of disease states but of the patient and his or her environment as it influences treatment outcomes and toxicities. These reasons and many others have led to a reevaluation of the generalizability of randomized trials and growing interest in accounting for this heterogeneity under the rubric of precision medicine as it relates to personalizing clinical care predictions, decisions, and therapy for the disease state. For the cancer patient treated with radiation therapy, characterizing the spatial treatment heterogeneity has been a fundamental tenet of routine clinical care facilitated by established database and imaging platforms. Leveraging these platforms to further characterize and collate all clinically relevant sources of heterogeneity that affect the longitudinal health outcomes of the irradiated cancer patient provides an opportunity to generate a critical informatics infrastructure on which precision radiation therapy may be realized. In doing so, data science-driven insight discoveries, personalized clinical decisions, and the potential to accelerate translational efforts may be realized ideally within a network of institutions with locally developed yet coordinated informatics infrastructures. The path toward realizing these goals has many needs and challenges, which we summarize, with many still to be realized and understood. Early efforts by our group have identified the feasibility of this approach using routine clinical data sets and offer promise that this transformation can be successfully realized in radiation oncology.

Revisiting the dose constraints for head and neck OARs in the current era of IMRT

Head and neck cancer poses a particular challenge in radiation therapy, whilst being an effective treatment modality it requires very high doses of radiation to provide effective therapy. This is further complicated by the fact that the head and neck region contains a large number of radiosensitive tissues, often resulting in patients experiencing debilitating normal tissue complications. In the era of intensity-modulated radiation therapy (IMRT) treatments can be delivered using non-uniform dose distributions selectively aimed at reducing the dose to critical organs-at-risk while still adequately covering the tumor target. Dose-volume constraints for the different risk organs play a vital role in one's ability to devise the best IMRT treatment plan for a head and neck cancer patient. To this end, it is pivotal to have access to the latest and most relevant dose constraints available and as such the goal of this review is to provide a summary of suggested dose-volume constraints for head and neck cancer RT that have been published after the QUANTEC reports were made available in early 2010.