Intensive Symptom Management for Head and Neck Cancer

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Roswell Park Cancer Institute

Disqualifiers: Metastatic cancer, Palliative therapy, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if closer monitoring of symptoms can improve the quality of life for individuals with head and neck cancer that hasn't spread. Participants will be divided into two groups: one will receive standard symptom care (Supportive Care), while the other will have more frequent check-ins through quality of life surveys, guided by intensive symptom surveillance and machine learning-directed risk stratification. This trial targets individuals diagnosed with non-metastatic squamous cell carcinoma of the head and neck who are about to begin radiation therapy. Those soon starting radiation treatment for this condition might be suitable candidates. As an unphased trial, this study provides a unique opportunity to contribute to innovative research that could enhance symptom management for future patients.

Do I have to stop taking my current medications for this trial?

The trial protocol does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that intensive symptom management is safe for head and neck cancer patients?

Research shows that closely monitoring symptoms can be crucial for patients with head and neck cancer, as it helps catch issues early. While specific safety data from past studies on this exact method is lacking, close symptom monitoring has been used in cancer care without major safety concerns.

Studies suggest that machine learning models can assist doctors in predicting patient outcomes by analyzing past data. This method, explored in cancer care, is generally considered safe because it doesn't involve direct treatment on the patient but aids doctors in making better decisions.

Both approaches in this trial aim to improve care without adding extra risks, focusing on observing and analyzing data rather than direct physical treatments. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores innovative ways to tackle symptom management for head and neck cancer using intensive symptom surveillance and machine learning-directed risk stratification. Unlike standard care, which typically involves regular symptom management during and after radiation therapy, this approach utilizes machine learning to predict and address potential complications more proactively. This could lead to more personalized care, potentially improving patients' quality of life by swiftly identifying those at higher risk and tailoring interventions accordingly.

What evidence suggests that this trial's treatments could be effective for head and neck cancer?

Research has shown that closely monitoring symptoms with machine learning can help predict outcomes for head and neck cancer patients. In this trial, some participants will receive machine learning-directed risk stratification to identify patients who might need more care by spotting those at higher risk. Studies have found that using AI to sort patients based on risk can improve treatment by predicting how patients will respond to therapy and what side effects they might experience. This technology aims to provide personalized care, making it more effective for each patient. Early findings suggest that this approach could lead to better management of symptoms in head and neck cancer patients. Meanwhile, other participants will receive standard symptom management as part of the trial's active comparator arm. ^{1 3 6 7 8}

Who Is on the Research Team?

Anurag K. Singh

Principal Investigator

Roswell Park Cancer Institute

Are You a Good Fit for This Trial?

Adults diagnosed with non-metastatic head and neck cancer, who can consent in English and are starting curative-intent radiation therapy soon. They must have a caregiver able to provide informed consent in English without payment. Pregnant individuals, those with metastatic cancer or only eligible for palliative care, or unable to follow the trial protocol cannot participate.

Inclusion Criteria

Informed caregiver not receiving any payment to provide care for patients

I have been diagnosed with a specific type of throat or mouth cancer that has not spread.

Participant must understand the investigational nature of this study and sign an Independent Ethics Committee/Institutional Review Board approved written informed consent form prior to receiving any study related assessment

Exclusion Criteria

Unwilling or unable to follow protocol requirements

I have been diagnosed with cancer that has spread from my head or neck.

Any condition which in the Investigator's opinion deems the participant an unsuitable candidate

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Radiation Therapy

Participants receive standard of care radiation therapy with symptom management

6 months

Weekly visits

Intensive Symptom Surveillance

Participants complete quality of life questionnaires twice weekly during radiation therapy and once weekly for the first month after completing radiation therapy

1 month

Twice weekly visits during radiation, weekly visits post-radiation

Follow-up

Participants are monitored for safety and effectiveness after treatment

6 months

Monthly visits

What Are the Treatments Tested in This Trial?

Interventions

• Intensive Symptom Surveillance
• Machine Learning-Directed Risk Stratification
• Supportive Care

Trial Overview The INSIGHT Trial is testing whether intensive symptom surveillance guided by machine learning improves patient outcomes compared to standard symptom management during and after radiation therapy for patients with non-metastatic head and neck cancers.

How Is the Trial Designed?

Group I: Group A (quality of life questionnaire) Experimental Treatment 2 Interventions

Patients complete a quality of life questionnaires over 10-15 minutes BIW during standard of care radiation therapy and QW for the first month after completing standard of care radiation therapy course, and then once monthly for 6 months.

Group II: Group B (standard symptom management) Active Control 1 Intervention

Patients receive standard symptom management QW during standard of care radiation therapy and for 6 months after completing radiation therapy course.

Who Is Running the Clinical Trial?

Roswell Park Cancer Institute

Lead Sponsor

Trials

427

Recruited

40,500+

Published Research Related to This Trial

The development of a data-driven visual system using sequential rule mining (SRM) allows for better prediction of long-term symptoms in head and neck cancer patients based on their treatment experiences, enhancing personalized care.

This system not only aids in understanding complex symptom patterns but also supports clinical decision-making by explaining predictions in the context of therapeutic choices, ultimately improving patient quality of life.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining. Floricel, C., Wentzel, A., Mohamed, A., et al.[2023]

In a study of 48 patients with head and neck cancer undergoing chemoradiotherapy, significant reductions in tumor volume were observed, with median changes of 26.8% for primary tumors and 43.0% for nodal tumors, indicating effective treatment response.

Two decision trees were developed to predict tumor volume reduction based on clinical and pathological parameters, achieving an accuracy of 88%, which can help radiation oncologists identify patients who may benefit most from adaptive radiotherapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy. Surucu, M., Shah, KK., Mescioglu, I., et al.[2017]

The study involving 30 head and neck cancer patients demonstrated that semi-automatic Quality of Life (QOL)-weighted NTCP-guided VMAT treatment plans effectively prioritized the sparing of organs at risk, leading to a significant reduction in complications like dysphagia and fatigue, while maintaining adequate target coverage.

Compared to conventional treatment plans, the QOL-weighted approach resulted in a systematic improvement in predicted QOL scores over 24 months, with an average increase of 1.1 points on a 0-100 scale, indicating enhanced patient well-being post-treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quality of life and toxicity guided treatment plan optimisation for head and neck cancer. van der Laan, HP., van der Schaaf, A., Van den Bosch, L., et al.[2021]

In a study of 65 patients undergoing head-and-neck radiotherapy, significant declines in quality of life (QOL) were observed, particularly in fatigue, social activity, and overall QOL, with 70.8% of patients reporting these issues during treatment.

The study highlighted that concurrent chemoradiotherapy (CCRT) led to more severe adverse events, including grade 3 complications in 35.4% of patients, significant weight loss averaging 5.5 kg, and a high need for supportive care interventions like intravenous hydration and feeding tube placement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electronic patient-reported outcomes and toxicities during radiotherapy for head-and-neck cancer. Niska, JR., Halyard, MY., Tan, AD., et al.[2023]

Patients with metastatic or locally recurrent head and neck cancer face significant challenges, including limited treatment options and high symptom burden, which complicates disease management and requires careful consideration of individual patient factors.

Recent phase III trials have shown that incorporating patient-reported outcome measures can help assess symptoms and quality of life, but there is still a need for better tools to evaluate treatment impact on symptom burden and functional impairment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

To treat or not to treat: balancing therapeutic outcomes, toxicity and quality of life in patients with recurrent and/or metastatic head and neck cancer. Murphy, BA.[2019]

The Vanderbilt Head and Neck Symptom Survey-Recurrent/Metastatic (VHNSS-RM) was developed to assess a wide range of symptoms and psychosocial issues in 50 patients with recurrent/metastatic head and neck cancer, identifying 12 physical symptoms and 7 psychosocial issues that significantly impact patients' quality of life.

The survey is designed to be feasible and not overly burdensome, suggesting it could effectively improve symptom management and palliative care for patients with recurrent/metastatic head and neck cancer.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Preliminary Testing of a Patient-Reported Outcome Measure for Recurrent or Metastatic Head and Neck Cancer. Jackson, LK., Deng, J., Ridner, SH., et al.[2017]

A study of oropharyngeal cancer patients identified four distinct clusters based on their symptom severity over six months, which correlated with their overall survival rates.

Patients in different clusters exhibited significant differences in factors such as HPV status, performance status, and treatment modalities, suggesting that tracking symptom severity can provide valuable prognostic information beyond traditional clinical characteristics.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Association of post-treatment longitudinal symptom severity clusters with subsequent survival in oropharyngeal cancer. Haddad, G., Hueniken, K., Xu, MC., et al.[2022]

In a study of 1589 head and neck cancer patients followed for over 10 years, those presenting with fewer symptoms had significantly better survival rates; median survival was 5.3 years for one symptom compared to just 1.1 years for three symptoms.

Specific symptoms also impacted survival: patients with weight loss had a median survival of only 0.8 years, while those with hoarseness had a median survival of 5.9 years, indicating that certain symptoms can serve as important indicators for prognosis.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Presenting symptoms and long-term survival in head and neck cancer. Douglas, CM., Ingarfield, K., McMahon, AD., et al.[2019]

The study successfully collected patient-reported outcomes electronically from 318 head and neck cancer patients undergoing radiotherapy, demonstrating that this method is feasible for tracking symptoms during treatment.

Patients reported significant symptoms during radiotherapy, with the most severe being dysgeusia (taste changes), pain, and mucositis, particularly among those with oropharynx cancer, highlighting the need for targeted symptom management in this population.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-reported outcome measures in patients undergoing radiotherapy for head and neck cancer. Hamilton, SN., Tran, E., Ho, C., et al.[2021]

The study identified two main symptom clusters in head and neck cancer patients undergoing chemoradiotherapy: one specific to head and neck symptoms (including radiodermatitis, dysphagia, and dry mouth) and another gastrointestinal cluster (including nausea and vomiting), based on data from 684 patients.

These symptom clusters were generally applicable across various patient subgroups, suggesting that understanding these clusters can enhance symptom management in clinical practice and highlight the need for further research into their biological mechanisms.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Symptom clusters in patients with head and neck cancer receiving concurrent chemoradiotherapy. Xiao, C., Hanlon, A., Zhang, Q., et al.[2021]

Citations

1. ascopubs.org ascopubs.org/doi/10.1200/EDBK-25-472464

Artificial Intelligence for Head and Neck Squamous Cell ... Such AI-based risk stratification can support oncologists in determining the acuity of care by flagging high-risk patients who may need urgent ...

2. clinicaltrials.gov clinicaltrials.gov/study/NCT05338905?term=AREA%5BInterventionSearch%5D(%22palliative%20care%22)&rank=2

Study Details | NCT05338905 | Intensive Symptom ... This clinical trial compares intensive symptom evaluation with supportive care to standard symptom management in patients with head and neck cancer that has ...

3. trialx.com trialx.com/clinical-trials/listings/252809/intensive-symptom-surveillance-guided-by-machine-learning-directed-risk-stratification-in-patients-with-non-metastatic-head-and-neck-cancer-the-insight-trial/

Intensive Symptom Surveillance Guided by Machine ... This trial may help researchers determine the impact of intensive symptom surveillance in patients with non-metastatic head and neck cancers.

4. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10014342/

Artificial intelligence to predict outcomes of head and neck ... Recent studies have shown promising results in the use of ML in the field of HNC RT in predicting therapeutic outcomes and toxicity.

5. appliedradiationoncology.com appliedradiationoncology.com/articles/recent-trends-of-artificial-intelligence-in-radiation-oncology-a-narrative-review-of-prospective-studies

A Narrative Review of Prospective Studies Intensive Symptom Surveillance Guided by Machine Learning-Directed Risk Stratification in Patients With Non-Metastatic Head and Neck Cancer ...

6. ascopubs.org ascopubs.org/doi/10.1200/EDBK_389718

Personalizing Surveillance in Head and Neck Cancer One study found that 87% of patients who survived after recurrence initially had T1 or T2 index tumors and only 30% had nodal disease at the ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC7005972/

Head and Neck Cancer Care in a Safety-Net Hospital Treatment delays and suboptimal adherence to posttreatment surveillance may adversely affect head and neck cancer (HNC) outcomes.

8. sciencedirect.com sciencedirect.com/science/article/pii/S0360301623082585

Imaging and Biomarker Surveillance for Head and Neck ... Surveillance for survivors of head and neck cancer (HNC) is focused on early detection of recurrent or second primary malignancies.