Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.

Background: No robust predictive biomarkers exist to identify non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapies. The aim of this study was to explore the role of delta-radiomics features in predicting the clinical outcomes of patients with advanced NSCLC who received ICI therapy. Methods: Data of 179 patients with advanced NSCLC (stages IIIB-IV) from two institutions (Database 1 =133; Database 2 =46) were retrospectively analyzed. Patients in the Database 1 were randomly assigned into training and validation dataset, with a ratio of 8:2. Patients in Database 2 were allocated into testing dataset. Features were selected from computed tomography (CT) images before and 6-8 weeks after ICI therapy. For each lesion, a total of 1,037 radiomic features were extracted. Lowly reliable [intraclass correlation coefficient (ICC) <0.8] and redundant (r>0.8) features were excluded. The delta-radiomics features were defined as the relative net change of radiomics features between two time points. Prognostic models for progression-free survival (PFS) and overall survival (OS) were established using the multivariate Cox regression based on selected delta-radiomics features. A clinical model and a pre-treatment radiomics model were established as well. Results: The median PFS (after therapy) was 7.0 [interquartile range (IQR): 3.4, 9.1] (range, 1.4-13.2) months. To predict PFS, the model established based on the five most contributing delta-radiomics features yielded Harrell's concordance index (C-index) values of 0.708, 0.688, and 0.603 in the training, validation, and testing databases, respectively. The median survival time was 12 (IQR: 8.7, 15.8) (range, 2.9-23.3) months. To predict OS, a promising prognostic performance was confirmed with the corresponding C-index values of 0.810, 0.762, and 0.697 in the three datasets based on the seven most contributing delta-radiomics features, respectively. Furthermore, compared with clinical and pre-treatment radiomics models, the delta-radiomics model had the highest area under the curve (AUC) value and the best patients' stratification ability. Conclusions: The delta-radiomics model showed a good performance in predicting therapeutic outcomes in advanced NSCLC patients undergoing ICI therapy. It provides a higher predictive value than clinical and the pre-treatment radiomics models.

A validated model to predict spread through air space in lung adenocarcinoma.

Background: Spread through air space (STAS) is currently considered to be a significant predictor of a poor outcome of pulmonary adenocarcinoma. Preoperative prediction of STAS is of great importance for treatment planning. The aim of the present study was to establish a nomogram based on computed tomography (CT) features for predicting STAS in lung adenocarcinoma and to assess the prognosis of the patients with STAS. Methods: A retrospective cohort study was performed in Wuhan Union Hospital from December 2015 to March 2021. The sample was divided into training and testing cohorts. Clinicopathologic and radiologic variables were recorded. The independent risk factors for STAS were determined by stepwise regression and then incorporated into the nomogram. Receiver operating characteristic (ROC) curves and calibration curves analysed by the Hosmer-Lemeshow test were used to evaluate the performance of the model. Decision curve analysis (DCA) was conducted to determine the clinical value of the nomogram. The Kaplan-Meier method was used for survival analysis and the multivariable Cox proportional hazards regression model was used to identify independent predictors for recurrence-free survival (RFS) and overall survival (OS). Results: The sample included 244 patients who underwent surgical resection for primary lung adenocarcinoma. The training cohort included 199 patients (68 STAS-positive and 131 STAS-negative patients), and the testing cohort included 45 patients (15 STAS-positive and 30 STAS-negative patients). The preoperative CT features associated with STAS were shape, ground-glass opacity (GGO) ratio and spicules. The nomogram including these three factors had good discriminative power, and the areas under the ROC curve were 0.875 and 0.922 for the training and testing data sets, respectively, with well-fitted calibration curves. DCA showed that the nomogram was clinically useful. STAS-positive patients had significantly worse OS and RFS than STAS-negative patients (both P<0.01). OS and RFS at 5-year for STAS-positive patients were 63.1% and 59.5%, respectively. Multivariate analysis showed that age [hazard ratio (HR), 1.1; 95% confidence interval (CI): 1.035-1.169; P=0.002], diameter (HR, 1.06; 95% CI: 1.04-1.11; P=0.03) and surgical margin (HR, 32.8; 95% CI: 6.8-158.3; P<0.001) were independent risk factors for OS. Adjuvant therapy (HR, 7.345; 95% CI: 2.52-21.41; P<0.001), N stage (N2) (HR, 0.239; 95% CI: 0.069-0.828; P=0.02) and surgical margin (HR, 15.6; 95% CI: 5.9-41.1; P<0.001) were found to be independent risk factors for RFS. Conclusions: The outcome of STAS-positive patients was worse. The nomogram incorporating the identified CT features could be applied to facilitate individualized preoperative prediction of STAS and selection of rational therapy.

Carrier-Free Self-Assembly Nano-Sonosensitizers for Sonodynamic-Amplified Cuproptosis-Ferroptosis in Glioblastoma Therapy.

Cuproptosis is a newly discovered form of programmed cell death significantly depending on the transport efficacy of copper (Cu) ionophores. However, existing Cu ionophores, primarily small molecules with a short blood half-life, face challenges in transporting enough amounts of Cu ions into tumor cells. This work describes the construction of carrier-free nanoparticles (Ce6@Cu NPs), which self-assembled by the coordination of Cu2+ with the sonosensitizer chlorin e6 (Ce6), facilitating sonodynamic-triggered combination of cuproptosis and ferroptosis. Ce6@Cu NPs internalized by U87MG cells induce a sonodynamic effect and glutathione (GSH) depletion capability, promoting lipid peroxidation and eventually inducing ferroptosis. Furthermore, Cu+ concentration in tumor cells significantly increases as Cu2+ reacts with reductive GSH, resulting in the downregulation of ferredoxin-1 and lipoyl synthase. This induces the oligomerization of lipoylated dihydrolipoamide S-acetyltransferase, causing proteotoxic stress and irreversible cuproptosis. Ce6@Cu NPs possess a satisfactory ability to penetrate the blood-brain barrier, resulting in significant accumulation in orthotopic U87MG-Luc glioblastoma. The sonodynamic-triggered combination of ferroptosis and cuproptosis in the tumor by Ce6@Cu NPs is evidenced both in vitro and in vivo with minimal side effects. This work represents a promising tumor therapeutic strategy combining ferroptosis and cuproptosis, potentially inspiring further research in developing logical and effective cancer therapies based on cuproptosis.

Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates.

BACKGROUND: Limited studies have investigated the accuracy of therapeutic decision-making using machine learning-based coronary computed tomography angiography (ML-CCTA) compared with CCTA. PURPOSE: To investigate the performance of ML-CCTA for therapeutic decision compared with CCTA. MATERIAL AND METHODS: The study population consisted of 322 consecutive patients with stable coronary artery disease. The SYNTAX score was calculated with an online calculator based on ML-CCTA results. Therapeutic decision-making was determined by ML-CCTA results and the ML-CCTA-based SYNTAX score. The therapeutic strategy and the appropriate revascularization procedure were selected using ML-CCTA, CCTA, and invasive coronary angiography (ICA) independently. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, accuracy of ML-CCTA and CCTA for selecting revascularization candidates were 87.01%, 96.43%, 95.71%, 89.01%, 91.93%, and 85.71%, 87.50%, 86.27%, 86.98%, 86.65%, respectively, using ICA as the standard reference. The area under the receiver operating characteristic curve (AUC) of ML-CCTA for selecting revascularization candidates was significantly higher than CCTA (0.917 vs. 0.866, P = 0.016). Subgroup analysis showed the AUC of ML-CCTA for selecting percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) candidates was significantly higher than CCTA (0.883 vs. 0.777, P < 0.001, 0.912 vs. 0.826, P = 0.003, respectively). CONCLUSION: ML-CCTA could distinguish between patients who need revascularization and those who do not. In addition, ML-CCTA showed a slightly superior to CCTA in making an appropriate decision for patients and selecting a suitable revascularization strategy.

Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

OBJECTIVES: To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. METHODS: Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. RESULTS: Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. CONCLUSION: The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. CLINICAL RELEVANCE STATEMENT: Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. KEY POINTS: • The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.

Prognostic MRI features to predict postresection survivals for very early to intermediate stage hepatocellular carcinoma.

OBJECTIVES: Contrast-enhanced MRI can provide individualized prognostic information for hepatocellular carcinoma (HCC). We aimed to investigate the value of MRI features to predict early (≤ 2 years)/late (> 2 years) recurrence-free survival (E-RFS and L-RFS, respectively) and overall survival (OS). MATERIALS AND METHODS: Consecutive adult patients at a tertiary academic center who received curative-intent liver resection for very early to intermediate stage HCC and underwent preoperative contrast-enhanced MRI were retrospectively enrolled from March 2011 to April 2021. Three masked radiologists independently assessed 54 MRI features. Uni- and multivariable Cox regression analyses were conducted to investigate the associations of imaging features with E-RFS, L-RFS, and OS. RESULTS: This study included 600 patients (median age, 53 years; 526 men). During a median follow-up of 55.3 months, 51% of patients experienced recurrence (early recurrence: 66%; late recurrence: 34%), and 17% died. Tumor size, multiple tumors, rim arterial phase hyperenhancement, iron sparing in solid mass, tumor growth pattern, and gastroesophageal varices were associated with E-RFS and OS (largest p = .02). Nonperipheral washout (p = .006), markedly low apparent diffusion coefficient value (p = .02), intratumoral arteries (p = .01), and width of the main portal vein (p = .03) were associated with E-RFS but not with L-RFS or OS, while the VICT2 trait was specifically associated with OS (p = .02). Multiple tumors (p = .048) and radiologically-evident cirrhosis (p < .001) were the only predictors for L-RFS. CONCLUSION: Twelve visually-assessed MRI features predicted postoperative E-RFS (≤ 2 years), L-RFS (> 2 years), and OS for very early to intermediate-stage HCCs. CLINICAL RELEVANCE STATEMENT: The prognostic MRI features may help inform personalized surgical planning, neoadjuvant/adjuvant therapies, and postoperative surveillance, thus may be included in future prognostic models. KEY POINTS: • Tumor size, multiple tumors, rim arterial phase hyperenhancement, iron sparing, tumor growth pattern, and gastroesophageal varices predicted both recurrence-free survival within 2 years and overall survival. • Nonperipheral washout, markedly low apparent diffusion coefficient value, intratumoral arteries, and width of the main portal vein specifically predicted recurrence-free survival within 2 years, while the VICT2 trait specifically predicted overall survival. • Multiple tumors and radiologically-evident cirrhosis were the only predictors for recurrence-free survival beyond 2 years.

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules.

Objectives: To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery. Methods: The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA). Results: The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model. Conclusion: DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.

Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis.

Background: Distinguishing synchronous double primary lung adenocarcinoma (SDPLA) from intrapulmonary metastasis (IPM) of lung cancer has significant therapeutic and prognostic values. This study aimed to develop and validate a CT-based radiomics model to differentiate SDPLA from IPM. Methods: A total of 153 patients (93 SDPLA and 60 IPM) with 306 pathologically confirmed lesions were retrospectively studied. CT morphological features were also recorded. Region of interest (ROI) segmentation was performed semiautomatically, and 1,037 radiomics features were extracted from every segmented lesion The differences of radiomics features were defined as the relative net difference in radiomics features between the two lesions on CT. Those low reliable (ICC <0.75) and redundant (r>0.9) features were excluded by intraclass correlation coefficients (ICC) and Pearson's correlation. Multivariate logistic regression (LR) algorithm was used to establish the classification model according to the selected features. The radiomics model was based on the four most contributing differences of radiomics features. Clinical-CT model and MixModel were based on selected clinical and CT features only and the combination of clinical-CT and Rad-score, respectively. Results: In both the training and testing cohorts, the area under the curves (AUCs) of the radiomics model were larger than those of the clinical-CT model (0.944 vs. 0.793 and 0.886 vs. 0.735 on training and testing cohorts, respectively), and statistically significant differences between the two models in the testing set were found (P<0.001). Meanwhile, three radiologists had sensitivities of 84.2%, 63.9%, and 68.4%, and specificities of 76.9%, 69.2%, and 76.9% in differentiating 19 SDPLA cases from 13 cases of IPM in the testing set. Compared with the performance of the three radiologists, the radiomics model showed better accuracy to the patients in both the training and testing cohorts. Among the three models, the radiomics model showed the best net benefits. Conclusions: The differences of radiomics features showed excellent diagnostic performance for preoperative differentiation between synchronous double primary lung adenocarcinoma from interpulmonary metastasis, superior to the clinical model and decisions made by radiologists.

Remediation of lead and cadmium co-contaminated mining soil by phosphate-functionalized biochar: Performance, mechanism, and microbial response.

The remediation of heavy metals contaminated soils is of great significance for reducing their risk to human health. Here, pristine pinewood sawdust biochar (BC) and phosphate-functionalized biochar (PBC) were conducted to investigate their immobilization performance towards lead (Pb) and cadmium (Cd) in arable soils severely polluted by Pb (9240.5 mg kg-1) and Cd (10.71 mg kg-1) and microbial response in soils. Compared to pristine BC (2.6-12.1%), PBC was more effective in immobilizing Pb and Cd with an immobilization effectiveness of 45.2-96.2% after incubation of 60 days. Moreover, the labile Pb and Cd in soils were transformed to more stable species after addition of PBC, reducing their bioavailability. The immobilization mechanisms of Pb and Cd by PBC were mainly to facilitate the formation of stable phosphate precipitates e.g., Cd3(PO4)2, Cd5(PO4)3OH, Cd5H2(PO4)4‧4H2O, and pyromorphite-type minerals. Further, PBC increased pH, organic matter, cation exchange capacity, and available nutrients (phosphorus and potassium) in soils. High-throughput sequencing analysis of 16 S rRNA genes indicated that the diversity and composition of bacterial community responded to PBC addition due to PBC-induced changes in soil physicochemical properties, increasing the relative abundance of beneficial bacteria (e.g., Brevundimonas, Bacillus, and norank_f__chitinophagaceae) in the treated soils. What's more, these beneficial bacteria could not only facilitate Pb and Cd immobilization but also alter nutrient biogeochemical transformation (nitrogen and iron) in co-contaminated soils. Overall, PBC could be a promising material for immobilization of Pb and Cd and the simultaneous enhancement of soil quality and available nutrients in co-contaminated mining soils.

Utility of Dark-Blood Dual-Energy CT Images for Predicting Vascular Involvement and R0 Resection in Patients With Pancreatic Cancer.

BACKGROUND. Current CT criteria for assessing vascular involvement by pancreatic ductal adenocarcinoma (PDAC) use circumferential contact as an indirect indicator. Dark-blood images derived from dual-energy CT (DECT) provide high lumen-to-wall contrast and may aid assessment. OBJECTIVE. The purpose of this study was to compare the diagnostic performance of 55-keV virtual monoenergetic images (VMIs) assessed using NCCN criteria with that of dark-blood images assessed using wall-based criteria for predicting vascular involvement and surgical resection that achieves microscopically negative margins (i.e., R0 resection) in patients with PDAC who undergo contrast-enhanced DECT. METHODS. This retrospective study included 109 patients (mean age, 62.6 ± 8.8 [SD] years; 66 men, 43 women) with histologically confirmed PDAC who underwent pancreatic parenchymal and portal venous phase DECT within 4 weeks before surgery (including PDAC resection in 73 patients) between July 2020 and June 2022. Dark-blood images were derived using a two-material decomposition algorithm. Two radiologists independently reviewed 55-keV VMIs and dark-blood images in separate sessions to evaluate celiac artery, common hepatic artery, superior mesenteric artery, portal vein, and superior mesenteric vein involvement; a third radiologist resolved discrepancies. On 55-keV VMIs, vessel relationships were classified as no contact, abutment (≤ 180° contact), or encasement (> 180° contact). On dark-blood images, vessel walls were categorized as intact circumferentially, irregular, or discontinuous. Tumor resectability status was classified on the basis of vessel relationships. Surgical observation served as the reference for vascular involvement. Margin status was determined for resected tumors. RESULTS. Across the five vessels, for predicting vascular involvement, abutment or encasement on 55-keV VMIs had sensitivity of 100.0% (all vessels) and specificity of 66.2-92.9%, and an irregular or discontinuous wall on dark-blood images had sensitivity of 80.0-100.0% and specificity of 88.2-98.0%. Specificity was higher for an irregular or discontinuous wall than for abutment or encasement for all vessels (all p < .05); sensitivity was not different for any vessel (all p > .05). Resectable disease classified by dark-blood images, compared with resectable disease classified by 55-keV VMIs, showed no difference in sensitivity (89.5% vs 78.9%, p = .33) but showed higher specificity (75.9% vs 59.3%, p = .01) for predicting R0 resection. CONCLUSION. Dark-blood images showed higher diagnostic performance than 55-keV VMIs for predicting vascular involvement and R0 resection in patients with PDAC. CLINICAL IMPACT. Dark-blood images may aid decisions regarding neoadjuvant therapy and surgical planning for PDAC.

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma.

Objectives: Spread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD. Methods: A total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC < 0.75) and redundant features (r > 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed. Results: The radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts. Conclusion: The CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.