The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project.

INTRODUCTION: To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision-making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision-making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients. METHODS: In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer-based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients. RESULTS: The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision-making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available. CONCLUSION: Future review efforts could utilize alternative search strategies to explore other lung cancer-specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision-making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.

INTRODUCTION: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). METHODS AND OBJECTIVES: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. CONCLUSION: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project.

Exploring the Role of Immunotherapy-Based Treatments for Advanced Non-Small-Cell Lung Cancer With Novel Driver Alterations.

BACKGROUND: Immunotherapy (IO) single agent or combined with chemotherapy (CT-IO) is the standard treatment for advanced non-small-cell lung cancer (aNSCLC) without driver alterations. IO efficacy in patients with novel driver alterations is not well reported. MATERIALS AND METHODS: Data of aNSCLC patients treated with IO or CT-IO in any line from January 2016 to September 2022 were retrospectively collected. Patients harboring novel driver alterations (m-cohort), including MET exon 14 skipping, BRAF (V600E or atypical), RET rearrangements, HER2 point mutations/exon 20 insertions or uncommon EGFR mutations/EGFR exon 20 insertions, and wild type patients (wt-cohort) were eligible. Clinico-pathological data were extracted from Institutional databases and compared through chi square or Fisher's exact test. Survivals were estimated through Kaplan-Meier method and compared by log-rank test. RESULTS: m-cohort and wt-cohort included 84 and 444 patients, respectively. Progression free survival (PFS) was 5.53 vs. 4.57 months (P= .846) and overall survival (OS) was 25.1 vs. 9.37 months, (P < .0001) for m-cohort compared to wt-cohort. Within the m-cohort, BRAF atypical mutations had the better outcomes (Overall Response Rate [ORR], PFS), targeted agents timing did not affect response to IO and CT-IO had better ORR and disease control rate (DCR) compared to IO single agent (P = .0160 and P = .0152). In the PD-L1≥50% group, first line IO single agent resulted in inferior ORR (P = .027) and PFS (P = .022) in m-cohort compared to wt-cohort. CONCLUSION: IO based treatments seem not detrimental for patients harboring novel driver alteration. Adding CT could improve modest responses to IO alone. Confirmation on larger datasets is required.