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Background: Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer. Materials and methods: A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy. Results: Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01). Conclusion: Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment.
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Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
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RATIONALE AND OBJECTIVES: Few reports have studied lung aeration and perfusion in normal lungs, COVID-19, and ARDS from other causes (NC-ARDS) using dual-energy computed tomography pulmonary angiograms (DE-CTPA). To describe lung aeration and blood-volume distribution using DE-CTPAs of patients with NC-ARDS, COVID-19, and controls with a normal DE-CTPA ("healthy lungs"). We hypothesized that each of these conditions has unique ranges of aeration and pulmonary blood volumes. MATERIALS AND METHODS: This retrospective, single-center study of DE-CTPAs included patients with COVID-19, NC-ARDS (Berlin criteria), and controls. Patients with macroscopic pulmonary embolisms were excluded. The outcomes studied were the (1) lung blood-volume in areas with different aeration levels (normal, ground glass opacities [GGO], consolidated lung) and (2) aeration/blood-volume ratios. RESULTS: Included were 20 patients with COVID-19 (10 milds, 10 moderate-severe), six with NC-ARDS, and 12 healthy-controls. Lung aeration was lowest in patients with severe COVID-19 24% (IQR13%-31%) followed by those with NC-ARDS 40%(IQR21%-46%). Blood-volume in GGO was lowest in patients with COVID-19 [moderate-severe:-28.6 (IQR-33.1-23.2); mild: -30.1 (IQR-33.3-23.4)] and highest in normally aerated areas in NC-ARDS -37.4 (IQR-52.5-30.2-) and moderate-severe COVID-19 -33.5(IQR-44.2-28.5). The median aeration/blood-volume ratio was lowest in severe COVID-19 but some values overlapped with those observed among patients with NC-ARDS. CONCLUSION: Severe COVID-19 disease is associated with low total aerated lung volume and blood-volume in areas with GGO and overall aeration/blood volume ratios, and with high blood volume in normal lung areas. In this hypothesis-generating study, these findings were most pronounced in severe COVID disease. Larger studies are needed to confirm these preliminary findings.