Improving clinical outcomes for patients with locally advanced non-small cell lung cancer treated with photon and proton radiotherapy

Objectives To identify mechanisms improving clinical outcomes for patients with unresectable locally advanced non-small-cell lung cancer (LA-NSCLC) treated with photon and proton radiotherapy. Strategies explored include 1. Investigating using routine healthcare datasets to estimate survival outcomes for patients with LA-NSCLC treated with definitive radiotherapy, in order to assess the effectiveness of current strategies; 2. Assessing the physical advantages of protons by conducting a retrospective planning study comparing volumetric modulated arc therapy (VMAT) and pencil beam scanning (PBS) proton plans of superior sulcus tumours (SSTs), a rare subset of LA-NSCLC; 3. Exploring potential biological advantages of protons by examining major cell death pathways following XRT, high and low linear energy transfer (LET) proton irradiation of NSCLC cells. Methods Workflow 1: LA-NSCLC patients receiving definitive radiotherapy were identified. For each, key time points (date of diagnosis, recurrence, death or last clinical encounter) were used to calculate overall survival (OS) and progression free survival (PFS) from manual-data (hospital notes) and compared to estimated OS and PFS from routine-data (electronic databases). Dataset correlations were then tested to establish if routine-data were a reliable proxy measure for manual-data. Workflow 2: Patients with SSTs treated with 4D radiotherapy were identified. Tumour motion was assessed and excluded if >5 mm. Comparative VMAT and PBS plans were generated retrospectively. Robustness analysis was assessed for both plans involving: 1. 5 mm geometric uncertainty scenarios, with an additional 3.5% range uncertainty for proton plans; 2. verification plans at breathing extremes. Comparative dosimetric and robustness analyses were carried out. Workflow 3: Human NSCLC cell lines were irradiated with single doses of 2-15 Gy photon radiotherapy, high- or low-linear energy transfer (LET) protons (12 keV/µm and 1 keV/µm, respectively) and analysed 24-144 hours post-irradiation. DNA damage foci and cell death mechanisms were investigated. Results Workflow 1: In forty-three patients, routine data underestimated PFS by 0.09 months (p=0.86; 95% CI -0.86-1.03) and OS by 1.02 months (p=0.00; 95% CI 0.34-1.69) but there was good correlation with a Pearson correlation coefficient of 0.94 (p=0.00, 95% CI 0.90-0.97) for PFS and 0.97 (p= 0.00, 95% CI 0.95-0.98) for OS. Workflow 2: In ten patients, both modalities achieved similar target coverage with mean clinical target volume D95 of 98.1% + 0.4 (97.5-98.8) and 98.4% + 0.2 (98.1-98.9) for PBS and VMAT plans, respectively. The same four PBS and VMAT plans failed robustness. Proton plans significantly reduced mean lung dose (by 21.9%), lung V5, V10, V20 (by 47.9%, 36.4%, 12.1%, respectively), mean heart dose (by 21.4%) and vertebra dose (by 29.2%) (p<0.05). Workflow 3: XRT predominantly induced mitotic catastrophe, autophagy and senescence. Senescence, established via the p53/p21 pathway, was the major cell death pathway by which protons more effectively reduce clonogenic potential compared to XRT in NSCLC cell lines. High LET protons at a dose of 10 Gy(RBE) resulted in the lowest cell survival. The mechanisms driving the LET- and dose-dependent senescence was unclear but did not appear to be related to differential DNA repair machineries. Conclusions Proton radiotherapy could be pivotal in improving outcomes in select cases of LA-NSCLC. These studies demonstrate that 1. survival-outcomes are reliably estimated by routine data and such a methodology could enable rapid outcomes analysis to keep pace with trial development; 2. robust PBS plans are achievable in carefully selected patients and considerable dose reductions to the lung, heart and thoracic vertebra are possible without compromising target coverage; 3. Identification of LET- and dose-dependent proton-induced cellular senescence may guide radiotherapy optimisation and drug-radiotherapy combinations, maximising tumour cell kill. This work contributes to important preliminary research required to understand the physical and biological strengths and weaknesses prior to trials

Predictive methods for Adaptive Radiation Therapy: effects of the organ motion, of the deformable registration algorithms and of the dose accumulation.

Il lavoro di ricerca è finanziato dal Ministero della Salute - Bando Giovani Ricercatori 2010 MoH (GR-2010-2318757) “Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments”. La ricerca ha sviluppato metodi predittivi per Adaptive Radiation Therapy. Il paziente è soggetto a macro-micro variazioni anatomiche intra-inter frazione e funzionali durante le fasi di preparazione del piano terapeutico e la ripetitività durante le sedute di radioterapia sono affette da fattori quali movimento d’organo e variazione morfologica, che possono influenzare il programma terapeutico. I sistemi avanzati di calcolo e accumulo di dose consentono la registrazione delle dosi erogate, tenendo in considerazione variazioni locali e globali. Il ricorso a tecnologiche e risorse umane infinite per verificare, istante per istante, la dose erogata a un singolo paziente, sarebbero impensabili nella pratica clinica. I modelli predittivi mediante reti neurali o epidemiologici contribuiscono al monitoraggio del paziente, mediante metodi fisici e statistici. Sono stati valutati fattori di movimento d’organo, le variazioni anatomiche, la deformazione delle immagini e l’accumulo di dose come principali elementi ed effetti nell’utilizzo di metodi predittivi. Questi fattori richiedono la validazione e sviluppo, mediante analisi Bayesiane e misure sperimentali che confermino qualità e accuratezza degli algoritmi. Lo sviluppo e utilizzo di metodiche di dose accumulation e la verifica delle dosi erogate, considerando movimento e deformazione, ha portato allo sviluppo di prototipi robotici, per dosimetria in-vivo e valutazione del movimento, mediante LEGO®. Lo sviluppo delle reti neurali, delle metodiche epidemiologiche e di Support Vector Machine ha consentito di estendere, mediante un progetto di data-mining, le metodologie a centri di livello nazionale. La ricerca mostra le criticità dei metodi predittivi dimostrando l’efficienza delle reti neurali e modelli epidemiologici, nei trattamenti avanzati del tumore della testa e collo, prostata, pancreas e polmone.The research is funded by the Ministry of Health - Young Researchers in 2010 MoH (GR-2010-2318757) "Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments". The research has developed predictive methods for Adaptive Radiation Therapy. The patient is subjected to macro-micro anatomical and functional variations during the preparation of the treatment plan and during radiation therapy treatments. They are affected by factors such as organs movement and morphological variations, which may influence the therapeutic program. The advanced systems for dose calculation and accumulation allow the recording of doses delivered, taking into account local and global variations. The use of technological and human resources endless to check, moment by moment, the dose delivered to an individual patient, would be unthinkable in clinical practice. Predictive models using neural networks or epidemiological contribute to the monitoring of the patient by physical and statistics methods. Were evaluated factors of organ motion, anatomical variations, deformable registration and dose accumulation as principal elements and effects in the use of predictive methods. These factors require validation and development, using Bayesian analysis and experimental measurements that confirm the quality and accuracy of the algorithms. The development and use of methods of dose accumulation and verification of doses delivered, considering movement and deformation, has led to the development of robotic prototypes for in-vivo dosimetry and evaluation of the movement, using LEGO. The development of neural networks, of epidemiological methods and Support Vector Machine have allowed extending, by means of a data-mining project, the methodologies in nationwide centers. Research shows the criticality of predictive methods proving the efficiency of neural networks and epidemiological models, for advanced treatments of the head and neck, prostate, pancreas and lung cancer

Urological Cancer 2021

Cancer of the urological sphere is a disease continuously increasing in numbers in the statistics of tumor malignancies in Western countries. Although this fact is mainly due to the contemporary increase of life expectancy of the people in these geographic areas, many other factors do contribute as well to this growth. Urological cancer is a complex and varied disease of different organs and mainly affects the male population. In fact, kidney, prostate, and bladder cancer are regularly included in the top-ten list of the most frequent neoplasms in males in most statistics. The female population, however, has also increasingly found itself affected by renal and bladder cancer in the last decade. Considering these altogether, urological cancer is a problem of major concern in developed societies. This Topic Issue of Cancers intends to shed some light into the complexity of this field and will consider all useful and appropriate contributions that scientists and clinicians may provide to improve urological cancer knowledge for patients’ benefit. The precise identification of the molecular routes involved, the diagnostic pathological criteria in the grey zones, the dilemma of T1G3 management, and the possible treatment options between superficial, nonmuscle-invasive and muscle-invasive diseases will be particularly welcomed in this Issue