Machine Learning for Predicting and Managing Quality of Life in Lung Cancer Immunotherapy Patients

Development of a Machine Learning-Based Risk Prediction Model and Stratified Management Strategies for Quality of Life in Lung Cancer Patients Undergoing Immunotherapy

The goal of this study is to explore whether health-related quality of life (HRQoL) can be used as a predictive indicator for lung cancer patients and to implement clinical interventions. The study addresses two main objectives:

Analyzing HRQoL data of lung cancer patients undergoing immunotherapy using machine learning clustering methods to explore data patterns and build an HRQoL early warning model (already developed).

Validating this HRQoL early warning model in real-world settings by classifying patients with different HRQoL characteristics and assessing the clinical value of the model

Study Overview

• Status: Not yet recruiting
• Conditions: Lung Cancer Patients
• Intervention / Treatment: Behavioral: Conventional care intervention; Behavioral: Symptom cluster-based care intervention

Detailed Description

Lung cancer is the leading cause of cancer incidence and mortality in China, and it holds the same position in the United States. Non-small cell lung cancer (NSCLC) is the most common histological type, accounting for approximately 85% of lung cancer cases. Treatment strategies based on pathology, molecular subtyping, and clinical staging include surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. In recent years, immunotherapy has been extensively researched and applied in lung cancer treatment. It works by blocking the binding of PD-L1 on tumor cells to PD-1 on T cells, thereby releasing the inhibition of T cell function and killing the tumor cells. Immunotherapy has become the standard treatment for advanced NSCLC without driver mutations, and it covers the entire spectrum of non-surgical locally advanced NSCLC consolidation therapy, perioperative neoadjuvant, and adjuvant therapy for early-stage NSCLC. However, not all patients benefit from immunotherapy, with only a small subset experiencing clinical benefit. Therefore, identifying resistance mechanisms, selecting populations that benefit from treatment, and overcoming therapy resistance are complex and challenging clinical issues that require collaboration among basic, translational, and clinical oncology research teams.

In 1993, the World Health Organization (WHO) introduced the concept of Quality of Life (QoL), which refers to an individual's perception of their position in life within their cultural and value system, relating to their goals, expectations, standards, and concerns. Few studies focus on cancer patients' QoL, particularly those using patient-reported outcomes (PRO) as a primary endpoint. Most clinical trials for cancer drugs use PROs as secondary or exploratory endpoints. There is limited research that considers PROs as the primary endpoint. Therefore, it is essential to further investigate the relationship between cancer patients' health-related quality of life and prognosis, as well as its relevance to immunotherapy. This would facilitate better early identification of immune-related adverse events and systematic management, improving treatment adherence, QoL, and ensuring optimal treatment outcomes.

This project aims to develop a risk warning model for health-related quality of life in lung cancer patients receiving immunotherapy based on machine learning. By using cluster analysis, the study will clean, validate, and analyze the health-related quality of life data from the QLQ-C30 and QLQ-LC13 questionnaires from clinical trials available on the Vivli Global Clinical Research Data Sharing and Analysis Platform. The goal is to identify the distribution characteristics of these data and explore whether patient-reported outcomes can predict the efficacy of immunotherapy, thus serving as biomarkers to identify potential beneficiaries of immunotherapy. Furthermore, based on a risk warning and stratified management approach, the project aims to design appropriate symptom intervention strategies for different PRO types in immunotherapy patients, ultimately helping healthcare providers better understand the symptom burden that lung cancer patients may experience during immunotherapy and offering practical guidance for symptom management.

• Study Type: Interventional
• Enrollment (Estimated): 200
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Jianguo zhou; Phone Number: +8618311543939; Email: jianguo.zhou@yahoo.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

1. Histologically diagnosed with lung cancer
2. Age over 18 years
3. Currently receiving immunotherapy for lung cancer
4. Good verbal communication ability
5. Informed consent signed by the patient or family member

Exclusion Criteria:

1. Cognitive impairment or mental illness
2. Other severe diseases

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Supportive Care
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Placebo Comparator: The group with milder symptoms and better quality of life; the group uses unsupervised machine learning to identify patients with severe symptoms and poor functionality who are receiving immunotherapy for non-small cell lung cancer, and implements a symptom cluster care intervention.	Behavioral: Conventional care intervention; Standard nursing intervention. This refers to routine clinical care without a specific care plan tailored to the patient's symptoms. For example, if a patient has symptoms, the nurse assists the patient in notifying the doctor but does not provide any special treatment themselves
Active Comparator: The group with more severe symptoms and poorer quality of life	Behavioral: Symptom cluster-based care intervention; The patient symptoms were surveyed to develop a symptom cluster care intervention plan. The specific steps were as follows: a research team was established, relevant literature was reviewed, and qualitative interviews were conducted. Guided by symptom management theory and the Knowledge-Attitude-Practice (KAP) behavior model, a draft of the care intervention was created. This draft was then refined through expert consultation to finalize the intervention plan.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
EORTC QLQ C30	The EORTC QLQ-C30 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30) is a tool used to assess the quality of life in cancer patients. It consists of 30 items covering functioning, symptoms, and overall health status. Scores for each item range from 0 to 4 or 0 to 7 (depending on the item type), with overall scores typically ranging from 0 to 100. Higher scores indicate better quality of life with fewer symptoms and better functioning, while lower scores reflect worse quality of life with more severe symptoms and poorer functioning.	Two weeks after the intervention
EORTC QLQ LC-13	The EORTC QLQ LC-13 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Lung Cancer Module 13) is a questionnaire used to assess the quality of life of lung cancer patients. It includes 13 items, with scores ranging from 0 to 4, and the total score ranges from 0 to 52. A higher score indicates more severe symptoms and poorer quality of life, while a lower score indicates better quality of life. The EORTC QLQ LC-13 is often used in conjunction with the EORTC QLQ-C30 scale, as they complement each other and provide a comprehensive assessment of the quality of life in lung cancer patients.	Two weeks after the intervention

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
ORR (Objective Response Rate)	ORR refers to the proportion of patients who achieve either a complete response (CR) or partial response (PR) during treatment. This endpoint reflects the effectiveness of the treatment in controlling the tumor. It is typically assessed through imaging techniques (e.g., CT, MRI) and scored based on RECIST (Response Evaluation Criteria in Solid Tumors) or similar criteria. ORR is a common efficacy endpoint in cancer clinical trials.	Two weeks after the intervention
PFS (Progression-Free Survival)	PFS refers to the length of time from the start of treatment until the tumor progresses or the patient dies, typically assessed through imaging or clinical evaluation. This endpoint reflects the effectiveness of the treatment in delaying disease progression. PFS is particularly useful in evaluating the clinical efficacy of new drugs or therapies. An extension of PFS is usually considered a sign of effective treatment.	Two weeks after the intervention

Collaborators and Investigators

• Sponsor: Second Affiliated Hospital of Zunyi Medical University
• Collaborators: Ministry of Education of the People's Republic of China, Department of Humanities and Social Sciences

Study record dates

Study Major Dates

• Study Start (Estimated): January 1, 2025
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): April 1, 2026

Study Registration Dates

• First Submitted: December 1, 2024
• First Submitted That Met QC Criteria: December 7, 2024
• First Posted (Actual): December 10, 2024

Study Record Updates

• Last Update Posted (Estimated): December 13, 2024
• Last Update Submitted That Met QC Criteria: December 9, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Keywords: immunotherapy; lung cancer; HRQOL
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 24YJCZH462

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: No IPD will be shared.

Study Data/Documents

1. Individual Participant Data Set

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No