Objective Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features. Methods Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model. Results For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%). Conclusions In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting. Received: 18 November 2021 Accepted after revision: 01 June 2022 Article published online: 17 August 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Objective: In this study, we aimed to evaluate the role of 18F-fluorodeoxyglucose PET/computerized tomography (¹⁸F-FDG PET/CT)-based radiomic features in the differentiation of infection and malignancy in consolidating pulmonary lesions and to develop a prediction model based on radiomic features. Material and methods: The images of 106 patients who underwent ¹⁸F-FDG PET/CT of consolidated lesions observed in the lung between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest of the lung lesions was determined and volumetric and textural features were obtained. Clinical and radiomic data were evaluated with machine learning algorithms to build a model. Results: There was a significant difference in all standardized uptake value (SUV) parameters and 26 texture features between the infection and cancer groups. The features with a correlation coefficient of less than 0.7 among the significant features were determined as SUV_mean, GLZLM_SZE, GLZLM_LZE, GLZLM_SZLGE and GLZLM_ZLNU. These five features were analyzed in the Waikato Environment for Knowledge Analysis program to create a model that could distinguish infection and cancer groups, and the model performance was found to be the highest with logistic regression (area under curve, 0.813; accuracy, 75.7%). The sensitivity and specificity values of the model in distinguishing cancer patients were calculated as 80.6 and 70.6%, respectively. Conclusions: In our study, we created prediction models based on radiomic analysis of ¹⁸F-FDG PET/CT images. Texture analysis with machine learning algorithms is a noninvasive method that can be useful in the differentiation of infection and malignancy in consolidating lung lesions in the clinical setting.

Objectives: This study makes a retrospective examination of exploring the prognostic value of (18)fluorine-fluorodeoxyglucose (F-18-FDG) positron emission tomography/computed tomography (PET/CT) related metabolic-volumetric variables, nutritional status, and immune and inflammatory markers on progression-free survival (PFS) and overall survival (OS) in advanced adenocarcinoma patients with positive epidermal growth factor receptor (EGFR) mutations undergoing EGFR tyrosine kinase inhibitor (TKI) therapy. Methods: A retrospective examination was made of patients diagnosed with lung adenocarcinoma who underwent F-18-FDG PET/CT imaging for staging maximum four weeks before starting treatment, between January 2015 and July 2020. Included in the study were 68 patients identified histopathologically to have locally advanced/metastatic EGFR mutation-positive adenocarcinoma, and who underwent EGFR TKI therapy. The laboratory data of the patients, obtained 15 days before imaging performed for PET/CT staging, were evaluated. Results: Metabolic tumor volume, modified Glasgow prognostic score and locally advanced disease were identified as independent prognostic parameters for PFS (p= 0.004, p=0.029, p=0.016, respectively). A univariate Cox regression analysis revealed albumin/alkaline phosphatase and tumor size to be significant parameters for prognosis (p=0.033, p=0.043, respectively). A multivariate Cox regression analysis revealed that none of the parameters were predictive or OS. Conclusion: The parameters of F-18-FIDG PET/CT, especially the volumetric parameters, were found to be strong prognostic factors with statistical significance for predicting PFS. We believe that these parameters are important prognostic markers that should be evaluated together in the management and follow-up of patients with EGFR mutation-positive adenocarcinoma.

Objective: The aim of this study is to determine the role of metabolic and volumetric parameters obtained from 18Fluorine-Fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) imaging on progression-free survival (PFS) and overall survival (OS) in patients with advanced nonsquamous cell lung carcinoma (NSCLC) with anaplastic lymphoma kinase (ALK) rearrangement. Methods: Pre and post-treatment PET/CT images of the ALK + NSCLC patients between January 2015 and July 2020 were evaluated. The highest standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values were obtained from pre-tyrosine kinase inhibitor (TKI) basal PET/CT (PET_pre) and post-TKI PET/CT (PET_post) images. Total MTV (tMTV) and total TLG (tTLG) values were calculated by summing MTV and TLG values in all tumor foci. The change (Δ) in pSUVmax, pMTV, pTLG, tMTV and tTLG before and after treatment was calculated.The relationship of these parameters with OS and PFS was analyzed. Results: tTLG_pre, tMTV_pre, pTLG_pre, pMTV_pre, ∆SUVmax, ∆tMTV and ∆tTLG values were found to be associated with OS; ∆tMTV, ∆tTLG, tTLG_pre, tMTV_pre, pTLG_pre and pMTV_pre were associated with PFS. The cutoff values in both predicting OS and PFS were calculated as −31.6 and 391.1 for ∆tMTV and tTLG_pre, respectively. In Cox regression analysis, ∆tMTV and stage for OS and ∆tMTV and tTLGpre for PFS were obtained as prognostic factors. Conclusions: Metabolic and volumetric parameters, especially TLG values in the whole body before treatment and change in whole body MTV value, obtained from PET/CT may be useful in predicting prognosis and determining treatment strategies for patients with advanced ALK + NSCLC.

Background/Objectives: Spread through air spaces (STAS) represents an aggressive invasion pattern in lung cancer and is associated with unfavorable oncologic outcomes. As STAS is currently identifiable only on postoperative pathology, reliable preoperative, noninvasive prediction remains a clinical challenge. This study aimed to evaluate the feasibility of predicting STAS using ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-derived radiomic and clinicoradiomic models. Methods: In this retrospective study, patients who underwent surgical resection for lung cancer with available preoperative ¹⁸F-FDG PET/CT imaging were analyzed. Radiomic features were extracted from intratumoral and peritumoral regions. Clinical, radiomic-only, and combined clinicoradiomic models were developed using LASSO-based feature selection and multivariable logistic regression. Model performance was evaluated using nested cross-validation, receiver operating characteristic analysis, calibration assessment, and decision curve analysis. Results: Radiomic features reflecting intratumoral metabolic characteristics and peritumoral tissue heterogeneity were significantly associated with STAS. The combined clinicoradiomic model demonstrated superior discriminative performance compared with the clinical and radiomic-only models (mean AUC ≈ 0.75), along with favorable calibration (Brier score = 0.20) and improved clinical net benefit across relevant threshold probabilities. Lower eosinophil count, lower SUVmin_tumor, and lower intratumoral SUV skewness emerged as independent predictors of STAS. Conclusions: Preoperative prediction of STAS in lung cancer is feasible using PET/CT-based radiomic analysis integrating intratumoral, peritumoral, and clinical features. This noninvasive approach provides biologically relevant information beyond conventional anatomical assessment and warrants further validation in prospective, multicenter cohorts.

Saygıdeğer okuyucular,Her şeyden önce size teşekkür etmek istiyorum, bilgi çağında, bilgiden yoksun, bilgiden uzak işini yapadurmak yerine, sağlık alanındaki inovasyon, teknoloji ve yapay zeka kulvarlarındaki merakınız ve hekimseniz hastanıza en doğru tanıyı koyup, en doğru tedaviyi vermek gayesi yada alan uzmanı iseniz de bu alanlarda gelişime inanarak okumayı seçtiğiniz, konfor alanınızı bozmayı kabul ettiğiniz için.Bu kitapta sağlığımızın geleceğinin büyük bir kısmını oluşturacak olan, sağlığın yapay zekasını size sunduk. Yapay zekanın sağlık alanındaki gelişmelerini, karar destek sistemlerini, büyük veriyi ve gelecek perspektifini en güncel bilgiler ışığında bu kitapta bulacaksınız.