Managing Cachexia in Head and Neck Cancer: a Systematic Scoping Review

Mäkitie, Antti; Alabi, Rasheed Omobolaji; Orell, Helena; Youssef, Omar; Almangush, Alhadi; Homma, Akihiro; Takes, Robert P.; López, Fernando; Bree, Remco de; Rodrigo, Juan P.; Ferlito, Alfio

doi:10.1007/s12325-022-02074-9

Cited by 18 publications

(23 citation statements)

References 64 publications

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“…Patients who are identified as being malnourished with loss of FFM might be considered for active physical therapy and nutritional consultation early on, before cachexia is established. 60,61 Among the strengths of this prospective cohort study are the considerable size of the cohort and the prospective nature of data collection. In addition, the information gathered on patient body weight 6 months prior to diagnosis adds further depth to our data and has only been reported in a few previous studies on HNSCC.…”

Section: Discussionmentioning

confidence: 99%

“…In order words, the use of repeated BIA measurements could be a tool to guide the development of a personalized approach in the nutritional treatment of patients with HNSCC. Patients who are identified as being malnourished with loss of FFM might be considered for active physical therapy and nutritional consultation early on, before cachexia is established 60,61 …”

Section: Discussionmentioning

confidence: 99%

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Pretreatment fat‐free mass index correlates with early death in patients with head and neck squamous cell carcinoma

Talani,

Astradsson,

Farnebo

et al. 2024

Head & Neck

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BackgroundA significant proportion of patients with head and neck squamous cell carcinoma (HNSCC) are malnourished at diagnosis. In this study, we investigated how pretreatment body mass index (BMI) and fat‐free mass index (FFMI) correlate with early death, and whether these measurements are useful markers of prognosis for risk stratification of head and neck cancer patients.MethodsPatients (n = 404) with newly diagnosed, curable HNSCC and WHO performance status 0–2 were prospectively included and met with a study representative before treatment initiation, as well as up to four follow‐up visits. All patients provided an estimate of body weight at 6 months prior to diagnosis. Bioelectrical impedance analysis (BIA) was performed for all patients before treatment initiation.ResultsMost patients had oropharyngeal (46%), oral cavity (28%), or laryngeal cancer (12%). Forty‐five (11%) patients met the standardized criteria for malnutrition according to the Global Leadership Initiative on Malnutrition (GLIM) at diagnosis. FFMI at diagnosis was lower in patients who died within 6 and 12 months after the start of treatment than in patients who survived these time points (p = 0.035 and p = 0.005, respectively).ConclusionsIn this study, pretreatment FFMI was an independent prognostic factor for death within 6 and 12 months after the start of treatment in patients with HNSCC. Pretreatment BMI was not an independent risk factor for death within 6 and 12 months after treatment termination. Thus, FFMI may be useful for risk stratification of patients with head and neck cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Pretreatment fat‐free mass index correlates with early death in patients with head and neck squamous cell carcinoma

Talani,

Astradsson,

Farnebo

et al. 2024

Head & Neck

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“…A recent publication has reported that higher energy intake is necessary for cachexic patients treated for head and neck cancer to maintain skeletal muscle mass. 120 However, another study found KD to be more effective than insulin in reversing weight loss, reducing tumor size, and promoting weight gain. 121 We recommend further research on this matter.…”

Section: Adverse Effects Contraindications and Issues Of Concern When...mentioning

confidence: 99%

The Role of Ketogenic Diet in the Treatment of Neuroblastoma

et al. 2023

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The ketogenic diet (KD) was initially used in 1920 for drug-resistant epileptic patients. From this point onward, ketogenic diets became a pivotal part of nutritional therapy research. To date, KD has shown therapeutic potential in many pathologies such as Alzheimer’s disease, Parkinson’s disease, autism, brain cancers, and multiple sclerosis. Although KD is now an adjuvant therapy for certain diseases, its effectiveness as an antitumor nutritional therapy is still an ongoing debate, especially in Neuroblastoma. Neuroblastoma is the most common extra-cranial solid tumor in children and is metastatic at initial presentation in more than half of the cases. Although Neuroblastoma can be managed by surgery, chemotherapy, immunotherapy, and radiotherapy, its 5-year survival rate in children remains below 40%. Earlier studies have proposed the ketogenic diet as a possible adjuvant therapy for patients undergoing treatment for Neuroblastoma. In this study, we seek to review the possible roles of KD in the treatment of Neuroblastoma.

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“…Multiple barriers to adoption exist, including the reliance on small, single-institution datasets, the lack of external validation and generalizability, insufficient transparency and reproducibility of ML research, and no clear superior predictive modeling strategies [109–110,111 ▪ ]. Significant variations exist in data collection methods and data is not open-access, which limit the combination of various sources of data for robust model training using large datasets [112].…”

Section: Important Limitations In Current Applications Of Machine Lea...mentioning

confidence: 99%

“…It is therefore important to include diversity and socioeconomic metrics in ML algorithms in HNC clinical sciences to ensure generalizability, improve accuracy, and reduce bias. A regulatory framework for data collection, model development, performance metrics, external validation, and reporting is necessary to facilitate the recommendation of models for clinical evaluation [112,118]. Risk of bias assessment tools can be applied to evaluate data quality [111 ▪ ].…”

Section: Important Limitations In Current Applications Of Machine Lea...mentioning

confidence: 99%

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

Srinivasan,

Liu,

Rameau

2023

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer. Recent findings Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer. Summary Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.

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Managing Cachexia in Head and Neck Cancer: a Systematic Scoping Review

Cited by 18 publications

References 64 publications

Pretreatment fat‐free mass index correlates with early death in patients with head and neck squamous cell carcinoma

Pretreatment fat‐free mass index correlates with early death in patients with head and neck squamous cell carcinoma

The Role of Ketogenic Diet in the Treatment of Neuroblastoma

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

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