Personalized Radiation Therapy (PRT) for Lung Cancer

Abstract

This chapter reviews and discusses approaches and strategies of personalized radiation therapy (PRT) for lung cancers at four different levels: (1) clinically established PRT based on a patient's histology, stage, tumor volume and tumor locations; (2) personalized adaptive radiation therapy (RT) based on image response during treatment; (3) PRT based on biomarkers; (4) personalized fractionation schedule. The current RT practice for lung cancer is partially individualized according to tumor histology, stage, size/location, and combination with use of systemic therapy. During-RT PET-CT image guided adaptive treatment is being tested in a multicenter trial. Treatment response detected by the during-RT images may also provide a strategy to further personalize the remaining treatment. Research on biomarker-guided PRT is ongoing. The biomarkers include genomics, proteomics, microRNA, cytokines, metabolomics from tumor and blood samples, and radiomics from PET, CT, SPECT images. Finally, RT fractionation schedule may also be personalized to each individual patient to maximize therapeutic gain. Future PRT should be based on comprehensive considerations of knowledge acquired from all these levels, as well as consideration of the societal value such as cost and effectiveness.

Metastases with definitive pathological diagnosis but no detectable primary tumor: A surveillance epidemiology and end results‐based study

“…This difference may be because metastatic breast cancer is more sensitive to endocrine therapy, chemotherapy or molecularly targeted drug therapy . The survival analysis indicated that radiation only had a beneficial effect for those with metastases from lung cancer, and radiation is one of the main treatment methods for lung cancer . However, radiation may deteriorate the condition of skin cancer, which is not sensitive to radiotherapy.…”

A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

“…Radiomics, as a non-invasive, quantitative, and low-cost approach, can objectively and comprehensively evaluate tumor heterogeneity by converting medical images into high-dimensional, mineable, and quantitative imaging features via high-throughput extraction of data-characterization algorithms (Aerts et al, 2014 ; Gillies et al, 2016 ). These features can reveal disease progression, providing valuable information for personalized therapy and decision-support (Chicklore et al, 2013 ; Cameron et al, 2016 ; Huynh et al, 2016 ; Jin and Kong, 2016 ; Kotrotsou et al, 2016 ; Parekh and Jacobs, 2016 ; Ginsburg et al, 2017 ; Lee et al, 2017 ; Marin et al, 2017 ; Scalco and Rizzo, 2017 ; Shafiq-Ul-Hassan et al, 2017 ). Previous studies have shown that the radiomics signature alone or merged with clinical parameters could enhance predictive accuracy in cancers (Huang Y. et al, 2016 ; Huang Y. Q. et al, 2016 ; Zhang et al, 2017 ).…”

A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

“…Radiomics, a noninvasive, quantitative, and low-cost approach, can objectively and comprehensively evaluate tumor heterogeneity by converting medical images into high-dimensional, mineable, and quantitative imaging features via high-throughput extraction of data-characterization algorithms [11,12]. These features have potential to reveal disease progression, thereby provide valuable information for personalized therapy and decision-support [13][14][15][16][17][18][19][20][21][22][23]. Previous studies have shown radiomics signature alone or merged with clinical parameters could enhance predictive accuracy in cancers [24][25][26].…”