The impact of high-order features on performance of radiomics studies in CT non-small cell lung cancer.

High-order radiomic features have been shown to produce high performance models in a variety of scenarios. However, models trained without high-order features have shown similar performance, raising the question of whether high-order features are worth including given their increased computational burden. This comparative study investigates the impact of high-order features on model performance in CT-based Non-Small Cell Lung Cancer (NSCLC) and the potential uncertainty regarding their application in machine learning. Three categories of features were retrospectively retrieved from CT images of 347 NSCLC patients: first- and second-order statistical features, morphological features and transform (high-order) features. From these, three datasets were constructed: a "low-order" dataset (Lo) which included the first-order, second-order, and morphological features, a high-order dataset (Hi), and a combined dataset (Combo). A diverse selection of datasets, feature selection methods, and predictive models were included for the uncertainty analysis, with two-year survival as the study endpoint. AUC values were calculated for comparisons and Kruskal-Wallis testing was performed to determine significant differences. The Hi (AUC: 0.41-0.62) and Combo (AUC: 0.41-0.62) datasets generate significantly (P < 0.01) higher model performance than the Lo dataset (AUC: 0.42-0.58). High-order features are selected more often than low-order features for model training, comprising 87 % of selected features in the Combo dataset. High-order features are a source of data that can improve machine learning model performance. However, its impact strongly depends on various factors that may lead to inconsistent results. A clear approach to incorporate high-order features in radiomic studies requires further investigation.

DCE-CT parameters as new functional imaging biomarkers at baseline and during immune checkpoint inhibitor therapy in patients with lung cancer - a feasibility study.

BACKGROUND: With the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors. METHODS: Thirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up. A single target lesion at baseline and subsequent follow-up examinations were enclosed in the DCE-CT. Blood volume deconvolution (BVdecon), blood flow deconvolution (BFdecon), blood flow maximum slope (BFMax slope) and permeability were assessed using overall survival (OS) and progression-free survival (PFS) as endpoints in Kaplan Meier and Cox regression analyses. RESULTS: High baseline Blood Volume (BVdecon) (> 12.97 ml × 100 g-1) was associated with a favorable OS (26.7 vs 7.9 months; p = 0.050) and PFS (14.6 vs 2.5 months; p = 0.050). At early follow-up on day seven a higher relative increase in BFdecon (> 24.50% for OS and > 12.04% for PFS) was associated with an unfavorable OS (8.7 months vs 23.1 months; p < 0.025) and PFS (2.5 vs 13.7 months; p < 0.018). The relative change in BFdecon (categorical) on day seven was a predictor of OS (HR 0.26, CI95: 0.06 to 0.93 p = 0.039) and PFS (HR 0.27, CI95: 0.09 to 0.85 p = 0.026). CONCLUSION: DCE-CT-identified parameters may serve as potential prognostic biomarkers at baseline and during early treatment in patients with NSCLC treated with immune checkpoint inhibitor therapy.

EBUS-TBNA in mediastinal staging of non-small cell lung cancer: comparison with pathological staging.

OBJECTIVE: Although EBUS-TBNA combined with EUS-FNA or EUS-B-FNA stands as the primary approach for mediastinal staging in lung cancer, guidelines recommend mediastinoscopy confirmation if a lymph node identified on chest CT or showing increased PET scan uptake yields negativity on these techniques. This study aimed to assess the staging precision of EBUS/EUS. METHODS: We conducted a retrospective study comparing the clinical staging of non-small cell lung cancer patients undergoing EBUS/EUS with their post-surgery pathological staging. We analyzed the influence of histology, location, tumor size, and the time lapse between EBUS and surgery. Patients with N0/N1 staging on EBUS/EUS, undergoing surgery, and with at least one station approached in both procedures were selected. Post-surgery, patients were categorized into N0/N1 and N2 groups. RESULTS: Among the included patients (n = 47), pathological upstaging to N2 occurred in 6 (12.8%). Of these, 4 (66.7%) had a single N2 station, and 2 (33.3%) had multiple N2 stations. The adenopathy most frequently associated with upstaging was station 7. None of the analyzed variables demonstrated a statistically significant difference in the occurrence of upstaging. PET scan indicated increased uptake in only one of these adenopathies, and only one was visualized on chest CT. CONCLUSIONS: Upstaging proved independent of the studied variables, and only 2 patients with negative EBUS/EUS would warrant referral for mediastinoscopy. Exploring other noninvasive methods with even greater sensitivity for detecting micrometastatic lymph node disease is crucial.

Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature.

BACKGROUND: Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established. METHODS: A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed. RESULTS: Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set. CONCLUSION: The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.

Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer.

PURPOSE: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches, shorter surveillance intervals, and intensified adjuvant treatments. This study aims to analyze lung cancer sites in CT images to predict potential recurrences in high-risk individuals. METHODS: We retrieved anonymized imaging and clinical data from an institutional database, focusing on patients who underwent curative pulmonary resections for non-small cell lung cancers. Our study used a deep learning model, the Mask Region-based Convolutional Neural Network (MRCNN), to predict cancer locations and assign recurrence classification scores. To find optimized trained weighted values in the model, we developed preprocessing python codes, adjusted dynamic learning rate, and modifying hyper parameter in the model. RESULTS: The model training completed; we performed classifications using the validation dataset. The results, including the confusion matrix, demonstrated performance metrics: bounding box (0.390), classification (0.034), mask (0.266), Region Proposal Network (RPN) bounding box (0.341), and RPN classification (0.054). The model successfully identified lung cancer recurrence sites, which were then accurately mapped onto chest CT images to highlight areas of primary concern. CONCLUSION: The trained model allows clinicians to focus on lung regions where cancer recurrence is more likely, acting as a significant aid in the detection and diagnosis of lung cancer. Serving as a clinical decision support system, it offers substantial support in managing lung cancer patients.

Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.

Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.

A non-enhancing, T2 fluid-attenuated inversion recovery hyperintense, diffusion-restricting brainstem lesion in an EGFR tyrosine kinase inhibitor-treated non-small-cell lung cancer patient.

Leptomeningeal metastasis (LM) is a devastating complication of malignancy. Diagnosis relies on both contrast enhancement on imaging and malignant cells in cerebral spinal fluid cytology. Though early detection and prompt intervention improves survival, the detection of LM is limited by false negatives. A rare brainstem imaging finding uncovered specifically in EGFR mutation-positive lung cancer patients may represent an early sign of LM. This sign demonstrates high signal on T2 fluid-attenuated inversion recovery and diffusion-weighted imaging sequences, but paradoxically lacks correlative contrast enhancement. Here we report a case of a 72-year-old female EGFR-positive lung cancer patient who developed this lesion following treatment with two first-generation EGFR tyrosine kinase inhibitors then showed subsequent response to osimertinib, an irreversible third-generation EGFR tyrosine kinase inhibitor.

A non-enhancing, T2 FLAIR hyperintense, diffusion-restricting brainstem lesion in an EGFR-positive lung cancer patient may represent an early indicator of leptomeningeal metastases.

Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.

Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and 18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.

Diffusion weighted MRI and apparent diffusion coefficient as a prognostic biomarker in evaluating chemotherapy-antiangiogenic treated stage IV non-small cell lung cancer: A prospective, single-arm, open-label, clinical trial (BevMar).

PURPOSE: When treating Lung Cancer, it is necessary to identify early treatment failure to enable timely therapeutic adjustments. The Aim of this study was to investigate whether changes in tumor diffusion during treatment with chemotherapy and bevacizumab could serve as a predictor of treatment failure. MATERIAL AND METHODS: A prospective single-arm, open-label, clinical trial was conducted between September 2014 and December 2020, enrolling patients with stage IV non-small cell lung cancer (NSCLC). The patients were treated with chemotherapy-antiangiogenic combination. Diffusion weighted magnetic resonance imaging (DW-MRI) was performed at baseline, two, four, and sixteen weeks after initiating treatment. The differences in apparent diffusion coefficient (ADC) values between pre- and post-treatment MRIs were recorded as Delta values (ΔADC). We assessed whether ΔADC could serve as a prognostic biomarker for overall survival (OS), with a five year follow up. RESULTS: 18 patients were included in the final analysis. Patients with a ΔADC value ≥ -3 demonstrated a significantly longer OS with an HR of 0.12 (95 % CI; 0.03- 0.61; p = 0.003) The median OS in patients with a ΔADC value ≥ -3 was 18 months, (95 % C.I; 7-46) compared to 7 months (95 % C.I; 5-9) in those with a ΔADC value < -3. CONCLUSION: Our findings suggest that early changes in tumor ADC values, may be indicative of a longer OS. Therefore, DW-MRI could serve as an early biomarker for assessing treatment response in patients receiving chemotherapy combined with antiangiogenic therapy.

Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness.

Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.

Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.

BACKGROUND: The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially accelerate TME analysis. The aims of this study were to (1) assess the feasibility of using hematoxylin and eosin (H&E)-stained whole slide images (WSI) to develop an AI model for evaluating the TME and (2) to characterize the TME of adenocarcinoma (ADCA) and squamous cell carcinoma (SCCA) in fibrotic and non-fibrotic lung. METHODS: The cohort was derived from chest CT scans of patients presenting with lung neoplasms, with and without background fibrosis. WSI images were generated from slides of all 76 available pathology cases with ADCA (n = 53) or SCCA (n = 23) in fibrotic (n = 47) or non-fibrotic (n = 29) lung. Detailed ground-truth annotations, including of stroma (i.e., fibrosis, vessels, inflammation), necrosis and background, were performed on WSI and optimized via an expert-in-the-loop (EITL) iterative procedure using a lightweight [random forest (RF)] classifier. A convolution neural network (CNN)-based model was used to achieve tissue-level multiclass segmentation. The model was trained on 25 annotated WSI from 13 cases of ADCA and SCCA within and without fibrosis and then applied to the 76-case cohort. The TME analysis included tumor stroma ratio (TSR), tumor fibrosis ratio (TFR), tumor inflammation ratio (TIR), tumor vessel ratio (TVR), tumor necrosis ratio (TNR), and tumor background ratio (TBR). RESULTS: The model's overall classification for precision, sensitivity, and F1-score were 94%, 90%, and 91%, respectively. Statistically significant differences were noted in TSR (p = 0.041) and TFR (p = 0.001) between fibrotic and non-fibrotic ADCA. Within fibrotic lung, statistically significant differences were present in TFR (p = 0.039), TIR (p = 0.003), TVR (p = 0.041), TNR (p = 0.0003), and TBR (p = 0.020) between ADCA and SCCA. CONCLUSION: The combined EITL-RF CNN model using only H&E WSI can facilitate multiclass evaluation and quantification of the TME. There are significant differences in the TME of ADCA and SCCA present within or without background fibrosis. Future studies are needed to determine the significance of TME on prognosis and treatment.

CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.

BACKGROUND: Programmed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expression in non-small cell lung cancers(NSCLCs). METHODS: 259 consecutive patients with pathological confirmed NSCLCs were retrospectively collected and divided into the training cohort and validation cohort according to the chronological order. The univariate and multivariate analyses were used to build the clinical model. Radiomics and deep learning features were extracted from preoperative non-contrast CT images. After feature selection, Radiomics score (Rad-score) and deep learning radiomics score (DLR-score) were calculated through a linear combination of the selected features and their coefficients. Predictive performance for PD-L1 expression was evaluated via the area under the curve (AUC) of receiver operating characteristic, the calibration curves, and the decision curve analysis. RESULTS: The clinical model based on Cytokeratin 19 fragment and lobulated shape obtained an AUC of 0.767(95% CI: 0.673-0.860) in the training cohort and 0.604 (95% CI:0.477-0.731) in the validation cohort. 11 radiomics features and 15 deep learning features were selected by LASSO regression. AUCs of the Rad-score were 0.849 (95%CI: 0.783-0.914) and 0.717 (95%CI: 0.607-0.826) in the training cohort and validation cohort, respectively. AUCs of DLR-score were 0.938 (95%CI: 0.899-0.977) and 0.818(95%CI:0.727-0.910) in the training cohort and validation cohort, respectively. AUCs of the DLR-score were significantly higher than those of the Rad-score and the clinical model. CONCLUSION: The CT-based deep learning radiomics signature could achieve clinically acceptable predictive performance for PD-L1 expression, which showed potential to be a surrogate imaging biomarker or a complement of immunohistochemistry assessment.

Comparison of the variability and diagnostic efficacy of respiratory-gated PET/CT based radiomics features with ungated PET/CT in lung lesions.

OBJECTIVES: To investigate the variability and diagnostic efficacy of respiratory-gated (RG) PET/CT based radiomics features compared to ungated (UG) PET/CT in the differentiation of non-small cell lung cancer (NSCLC) and benign lesions. METHODS: 117 patients with suspected lung lesions from March 2020 to May 2021 and consent to undergo UG PET/CT and chest RG PET/CT (including phase-based quiescent period gating, pQPG and phase-matched 4D PET/CT, 4DRG) were prospectively included. 377 radiomics features were extracted from PET images of each scan. Paired t test was used to compare UG and RG features for inter-scan variability analysis. We developed three radiomics models with UG and RG features (i.e. UGModel, pQPGModel and 4DRGModel). ROC curves were used to compare diagnostic efficiencies, and the model-level comparison of diagnostic value was performed by five-fold cross-validation. A P value < 0.05 was considered as statistically significant. RESULTS: A total of 111 patients (average age ± standard deviation was 59.1 ± 11.6 y, range, 29 - 88 y, and 63 were males) with 209 lung lesions were analyzed for features variability and the subgroup of 126 non-metastasis lesions in 91 patients without treatment before PET/CT were included for diagnosis analysis. 101/377 (26.8 %) 4DRG features and 82/377 (21.8 %) pQPG features showed significant difference compared to UG features (both P<0.05). 61/377 (16.2 %) and 59/377 (15.6 %) of them showed significantly better discriminant ability (ΔAUC% (i.e. (AUCRG - AUCUG) / AUCUG×100 %) > 0 and P<0.05) in malignant recognition, respectively. For the model-level comparison, 4DRGModel achieved the highest diagnostic efficacy (sen 73.2 %, spe 87.3 %) compared with UGModel (sen 57.7 %, spe 76.4 %) and pQPGModel (sen 63.4 %, spe 81.8 %). CONCLUSION: RG PET/CT performs better in the quantitative assessment of metabolic heterogeneity for lung lesions and the subsequent diagnosis in patients with NSCLC compared with UG PET/CT.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Role of SFRP5 in Non-Small Cell Lung Cancer and Its Correlation with SUV of 18F-FDG PET-CT.

Aim: This study aimed to evaluate the relationship between secreted frizzled-related protein 5 (SFRP5) expression and fluorine 18-fluoro-deoxyglucose (18 F-FDG) uptake imaged with positron emission tomography/tomography (PET/CT) in patients with non-small cell lung cancer (NSCLC). In addition, we sought to elucidate the potential role and mechanism of action of SFRP5 in NSCLC.Materials and methods: The maximum standardized uptake value (SUVmax) of the lesions was calculated. SFRP5 expression was analyzed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). The correlation between SFRP5 expression and SUVmax was evaluated using Pearson's correlation analysis. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), flow cytometry, wound healing, and transwell assays were used to analyze cell viability, apoptosis, migration, and invasion, respectively.Results and conclusion: The results indicated that the SUVmax was higher in patients with NSCLC than that in healthy volunteers. Moreover, SFRP5 expression was lower in tissues from the four types of NSCLC than that in the adjacent normal tissues. SUVmax negatively correlated with SFRP5 expression in the four types of NSCLC. In addition, up-regulation of SFRP5 decreased the viability, migration, and invasion abilities, and increased apoptosis of NSCLC cells. Furthermore, SFRP5 inhibited the Wnt/ß-catenin pathway in NSCLC cells. In conclusion, SFRP5 modulates the biological behaviors of NSCLC through Wnt/ß-catenin pathway.

Prognostic potential of integrated morphologic and metabolic parameters of pre-therapeutic [18F]FDG-PET/CT regarding progression-free survival (PFS) and overall survival (OS) in NSCLC-patients.

PURPOSE: This study aimed to evaluate the prognostic potential of pre-therapeutic [18F]FDG-PET/CT variables regarding prediction of progression-free survival (PFS) and overall survival (OS) in NSCLC-patients. METHOD: NSCLC-patients who underwent pre-therapeutic [18F]FDG-PET/CT were retrospectively analyzed. The following imaging features were collected from the primary tumor: tumor size, tumor density, central necrosis, spicules and SUVmax. For standardization, an indexSUVmax was calculated (SUVmax primary tumor/SUVmax liver). Descriptive statistics and correlations of survival time analyses for PFS and OS were calculated using the Kaplan-Meier method and Cox regression including a hazard ratio (HR). A value of p < 0.05 was set as statistically significant. The 95%-confidence intervals (CI) were calculated. The median follow-up time was 63 (IQR 27-106) months. RESULTS: This study included a total of 82 patients (25 women, 57 men; mean age: 66 ± 9 years). IndexSUVmax (PFS: HR = 1.0, CI: 1.0-1.1, p = 0.49; OS: HR = 1.0, CI: 0.9-1.2, p = 0.41), tumor size (PFS: HR = 1.0, CI: 0.9-1.0, p = 0.08; OS: HR = 1.0, CI: 0.9-1.0, p = 0.07), tumor density (PFS: HR = 0.9, CI: 0.6-1.4, p = 0.73; OS: HR = 0.3; CI: 0.1-1.1; p = 0.07), central necrosis (PFS: HR = 1.0, CI: 0.6-1.8, p = 0.98; OS: HR = 0.6, CI: 0.2-1.9, p = 0.40) and spicules (PFS: HR = 1.0, CI: 0.6-1.9, p = 0.91; OS: HR = 1.3, CI: 0.4-3.7, p = 0.65) did not significantly affect PFS and OS in the study population. An optimal threshold value for the indexSUVmax was determined by ROC analysis and Youden's index. There was no significant difference in PFS with an indexSUVmax-threshold of 3.8 (13 vs. 27 months; p = 0.45) and in OS with an indexSUVmax-threshold of 4.0 (113 vs. 106 months; p = 0.40). CONCLUSIONS: SUVmax and morphologic parameters from pre-therapeutic [18F]FDG-PET/CT were not able to predict PFS and OS in NSCLC-patients.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Associations between Radiomics and Genomics in Non-Small Cell Lung Cancer Utilizing Computed Tomography and Next-Generation Sequencing: An Exploratory Study.

BACKGROUND: Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC). METHODS: This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan-Meier curves and Log-rank tests. RESULTS: Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39-20.48). CONCLUSIONS: The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.