Tomography Radiomics Research Articles

Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches.

To compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) and further evaluate the stratified effect of the radiomics model on disease-free survival (DFS) and overall survival (OS) of LSCC patients. In all, 106 LSCC patients (40 with inflamed and 66 with non-inflamed immunophenotyping) were randomly assigned into a training (n = 53) and testing (n = 53) cohort. Briefly, 750 radiomics features from contrast-enhanced CT images were respectively extracted from the whole tumor and two Otsu method-derived subregions. Intraclass correlation coefficients (ICCs) were calculated to evaluate the reproducibility. The radiomics models for predicting immunophenotyping were respectively created using K-nearest neighbors (KNN), logistic regression (LR), and Naive bayes (NB) classifiers. The performance of models in the testing cohort were compared using area under the curve (AUC). The prognostic value of the optimal model was determined by survival analysis. The radiomics features derived from whole tumor showed better reproducibility than those derived from habitats. The best model for the whole tumor (LR classifier) showed superior performance than that for the habitats (KNN classifier) in the testing cohort, but there were no significant differences (AUC: 0.741 vs. 0.611, p = 0.112). Multivariable Cox regression analysis showed that the immunophenotyping predicted by the optimal model was an independent risk factor of unfavorable DFS (p = 0.009) and OS (p = 0.008) in LSCC patients. Whole tumor-based CT radiomics could serve as a potential predictive biomarker of immunophenotyping and outcome prediction in LSCC patients.

PET/computed tomography radiomics combined with clinical features in predicting sarcopenia and prognosis of diffuse large B-cell lymphoma.

The study aimed to assess the role of 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) radiomics combined with clinical features using machine learning (ML) in predicting sarcopenia and prognosis of patients with diffuse large B-cell lymphoma (DLBCL). A total of 178 DLBCL patients (118 and 60 applied for training and test sets, respectively) who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Clinical characteristics and PET/CT radiomics features were analyzed, and feature selection was performed using univariate logistic regression and correlation analysis. Sarcopenia prediction models were built by ML algorithms and evaluated. Besides, prognostic models were also developed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified. Fourteen features were finally selected to build sarcopenia prediction and prognosis models, including two clinical (maximum standard uptake value of muscle and BMI), nine PET (seven gray-level and two first-order), and three CT (three gray-level) radiomics features. Among sarcopenia prediction models, combined clinical-PET/CT radiomics features models outperformed other models; especially the support vector machine algorithm achieved the highest area under curve of 0.862, with the sensitivity, specificity, and accuracy of 79.2, 83.3, and 78.3% in the test set. Furthermore, the consistency index based on the prognostic models was 0.753 and 0.807 for PFS and OS, respectively. The enrolled patients were subsequently divided into high-risk and low-risk groups with significant differences, regardless of PFS or OS (P < 0.05). ML models incorporating clinical and PET/CT radiomics features could effectively predict the presence of sarcopenia and assess the prognosis in patients with DLBCL.

Prognostic evaluation using radiomics after stereotactic body radiotherapy in early-stage lung cancer

Non-small cell lung cancer (NSCLC), the leading cause of cancer-related deaths, is the most common subtype of lung cancer with an incidence of 85%. Stereotactic body radiotherapy (SBRT) is a curative treatment option for patients with early-stage NSCLC who cannot undergo surgery due to medical reasons or who refuse surgery. Radiomics non-invasively extracts advanced imaging features invisible to the human eye from medical images. Radiomics has prognostic value in predicting oncological outcomes after lung SBRT. Although studies on this subject are available in the literature, they are quite heterogeneous. There is a need for large-scale multicenter studies in which standard imaging techniques are used to obtain radiomic features, artificial intelligence-based segmentations are used to eliminate differences between contours, and SBRT dose schemes with appropriate therapeutic indexes are applied. This review aimed to interpret the existing studies and emphasize the clinical importance of radiomics, which can contribute to personalized treatment. A comprehensive literature search was conducted through the PubMed database using a wide range of keywords, which yielded 11 peer-reviewed articles published between 2017 and 2024. Seven articles evaluated computed tomography radiomics, and four evaluated fluorodeoxyglucose positron emission tomography-computed tomography radiomics. Oncological outcomes are not always identical in patients with a similar history receiving similar treatments at the same stage and age. Clinical, demographic, or treatment-related data are insufficient to predict prognosis and determine personalized treatment. Incorporating radiomics to these data can help establish models with higher accuracy and achieve personalized treatment.

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression.

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

Combination of FDG PET/CT radiomics and clinical parameters for outcome prediction in patients with non-Hodgkin's lymphoma.

The purposes was to build model incorporating PET + computed tomography (CT) radiomics features from baseline PET/CT + clinical parameters to predict outcomes in patients with non-Hodgkin lymphomas. Cohort of 138 patients with complete clinical parameters and follow up times of 25.3 months recorded. Textural analysis of PET and manual correlating contouring in CT images analyzed using LIFE X software. Defined outcomes were overall survival (OS), disease free-survival, radiotherapy, and unfavorable response (defined as disease progression) assessed by end of therapy PET/CT or contrast CT. Univariable and multivariable analysis performed to assess association between PET, CT, and clinical. Male (P = 0.030), abnormal lymphocytes (P = 0.030), lower value of PET entropy (P = 0.030), higher value of SHAPE sphericity (P = 0.002) were significantly associated with worse OS. Advanced stage (III or IV, P = 0.013), abnormal lymphocytes (P = 0.032), higher value of CT gray-level run length matrix (GLRLM) LRLGE mean (P = 0.010), higher value of PET gray-level co-occurrence matrix energy angular second moment (P < 0.001), and neighborhood gray-level different matrix (NGLDM) busyness mean (P < 0.001) were significant predictors of shorter DFS. Abnormal lymphocyte (P = 0.033), lower value of CT NGLDM coarseness (P = 0.082), and higher value of PET GLRLM gray-level nonuniformity zone mean (P = 0.040) were significant predictors of unfavorable response to chemotherapy. Area under the curve for the three models (clinical alone, clinical + PET parameters, and clinical + PET + CT parameters) were 0.626, 0.716, and 0.759, respectively.

Delta-CT radiomics based model for predicting postoperative anastomotic leakage following radical resection of esophageal squamous cell carcinoma.

To predict postoperative anastomotic leakage (AL) following radical resection of esophageal squamous cell carcinoma (ESCC) based on clinical data and preoperative enhanced Computed tomography(CT) radiomics of the esophagus. We retrospectively analyzed the clinicopathological and radiological data of 213 patients with ESCC who received radical resection at Xiangyang No.1 People's Hospital from July 2011 to February 2024. 3D slicer software was used in combination with Lasso extraction and 10-fold cross-validation to extract texture parameters from contrast-enhanced CT images and generate Delta-Radscores. Several models were built using logistic regression to predict postoperative AL in ESCC. In the training set, the univariate analysis confirmed that duration of surgery, surgical method, delta radscore 1, delta radscore 2, contrast enhancement patterns, peripheral lymph node metastasis, post thoracotomy pulmonary infection(PTPI), and hot pot were risk factors for ESCC-AL (P<0.05 for both). The multivariate analysis showed that delta radscore 1, delta radscore 2, PTPI, and hot pot were independent risk factors for AL (P<0.05 for all). These results were verified by the XGboost machine learning model. The combinational model based on all of the above risk factors [AUC 0.900, OR 0.0282, 95%CI 0.841-0.943] outperformed either the clinical model[AUC 0.759, OR 0.0392, 95%0.683-0.825,P<0.05] or the imaging model[AUC 0.869, OR 0.0335, 95%0.804-0.918,P=0.1277] alone in predictive efficacy. The decision curve proved that the combinational model had a higher clinical net benefit. The nomogram generated via the combinational model simplified the predictive process. The same predictions were verified in the testing set. Delta radscore 1, delta radscore 2, PTPI, and hot pot were related to ESCC-AL. The novel nomogram created using enhanced CT radiomics informed perioperative management and improved the survival quality of ESCC patients.

Node Reporting and Data System Combined With Computed Tomography Radiomics Can Improve the Prediction of Nonenlarged Lymph Node Metastasis in Gastric Cancer.

To investigate the diagnostic performance of Node Reporting and Data System (Node-RADS) combined with computed tomography (CT) radiomics for assessing nonenlargement regional lymph nodes in gastric cancer (GC). Preoperative CT images were retrospectively collected from 376 pathologically confirmed of gastric adenocarcinoma from January 2019 to December 2023, with 605 lymph nodes included for analysis. They were divided into training (n = 362) and validation (n = 243) sets. Radiomics features were extracted from venous-phase, and the radiomics score was obtained. Clinical information, CT parameters, and Node-RADS classification were collected. A combined model was built using machine-learning approach and tested in validation set using receiver operating characteristic curve analysis. Further validation was conducted in different subgroups of lymph node short-axis diameter (SD) range. Node-RADS score, SD, maximum diameter of thickness of tumor, and radiomics were identified as the most predictive factors. The results demonstrated that the integrated model combining SD, maximum diameter of thickness of tumor, Node-RADS, and radiomics outperformed the model excluding radiomics, yielding an area under the receiver operating characteristic curve of 0.82 compared with 0.79, with a statistically significant difference (P < 0.001). Subgroup analysis based on different SDs of lymph nodes also revealed enhanced diagnostic accuracy when incorporating the radiomics score for the 4- to 7.9-mm subgroups, all P < 0.05. However, for the 8- to 9.9-mm subgroup, the combination of the radiomics did not significantly improve the prediction, with an area under the receiver operating characteristic curve of 0.85 versus 0.85, P = 0.877. The integration of radiomics scores with Node-RADS assessments significantly enhances the accuracy of lymph node metastasis evaluation for GC. This combined model is particularly effective for lymph nodes with smaller standard deviations, yielding a marked improvement in diagnostic precision. The findings of this study indicate that a composite model, which incorporates Node-RADS, radiomics features, and conventional parameters, may serve as an effective method for the assessment of nonenlarged lymph nodes in GC.

Texture analysis can predict response to etoposide-doxorubicin-cisplatin in patients with adrenocortical carcinoma.

The adrenocortical carcinoma (ACC) is a rare and highly aggressive malignancy originating from the adrenal cortex. These patients usually undergo chemotherapy with etoposide, doxorubicin, cisplatin and mitotane (EDP-M) in case of locally advanced or metastatic ACC. Computed tomography (CT) radiomics showed to be useful in adrenal pathologies. The study aimed to analyze the association between response to EDP-M treatment and CT textural features at diagnosis in patients with locally advanced or metastatic ACCs. We enrolled 17 patients with advanced or metastatic ACC who underwent CT before and after EDP-M therapy. The response to treatment was evaluated according to RECIST 1.1, Choi, and volumetric criteria. Based on the aforementioned criteria, the patients were classified as responders and not responders. Textural features were extracted from the biggest lesion in contrast-enhanced CT images with LifeX software. ROC curves were drawn for the variables that were significantly different (p < 0.05) between the two groups. Long-run high grey level emphasis (LRHGLE_GLRLM) and histogram kurtosis were significantly different between responder and not responder groups (p = 0.04) and the multivariate ROC curve combining the two features showed a very good AUC (0.900; 95%IC: 0.724-1.000) in discriminating responders from not responders. More heterogeneous tissue texture of initial staging CT in locally advanced or metastatic ACC could predict the positive response to EDP-M treatment. Adrenal texture is able to predict the response to EDP-M therapy in patients with advanced ACC.

DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.

Glycolysis proved to have a prognostic value in lung cancer; however, to identify glycolysis-related genomic markers is expensive and challenging. This study aimed at identifying glycolysis-related computed tomography (CT) radiomics features to develop a deep-learning prognostic model for non-small cell lung cancer (NSCLC). The study included 274 NSCLC patients from cohorts of The Second Affiliated Hospital of Soochow University (SZ; n=64), the Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), and the Gene Expression Omnibus dataset (n=136). Initially, the glycolysis enrichment scores were evaluated using a single-sample gene set enrichment analysis, and the cut-off values were optimized to investigate the prognostic potential of glycolysis genes. Radiomic features were then extracted using LIFEx software. The least absolute reduction and selection operator (LASSO) algorithm was employed to determine the glycolytic CT radiomics features. A deep-learning prognostic model was constructed by integrating CT radiomics and clinical features. The biological functions of the model were analyzed by incorporating RNA sequencing data. Kaplan-Meier curves indicated that elevated glycolysis levels were associated with poorer survival outcomes. The LASSO algorithm identified 11 radiomic features that were then selected for inclusion in the deep-learning model. They have shown significant discrimination capability in assessing glycolysis status, achieving an area under the curve value of 0.8442. The glycolysis-based radiomics deep-learning model was named the DeepGR model. This model was able to effectively predict the clinical outcomes of NSCLC patients with AUCs of 0.8760 and 0.8259 in the SZ and TCGA cohorts, respectively. High-risk DeepGR scores were strongly associated with poor overall survival, resting memory CD4+ T cells, and a high response to programmed cell death protein 1 immunotherapy. The DeepGR model effectively predicted the prognosis of NSCLC patients.

Predicting prognosis in patients with stage IA lung adenocarcinoma with a micropapillary component using a nomogram based on computed tomography radiomics and clinicopathologic factors: a retrospective analysis.

Patients with stage IA lung adenocarcinoma (ADC) with an micropapillary (MIP) component are at a higher risk of recurrence after radical surgical resection; however, adding adjuvant chemotherapy (ACT) to their postoperative course remains controversial. This study determined the predictive factors that influence the prognosis of these patients and identified those at high risk of recurrence. Between January 2012 and December 2018, 254 eligible patients with stage IA lung ADC with an MIP component were categorized into training (n=169) and validation (n=85) cohorts. Clinicopathological and radiomics features were included in univariate and multivariate analyses, and statistically significant predictors were used to develop the nomogram. Area under the curve (AUC) of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to validate the model. The calculated risk scores for each patient were risk-stratified using the X-tile procedure, and survival analyses were performed among the different risk subgroups. T1c stage, MIP ≥1%, spread through air space (STAS), carcinoembryonic antigen (CEA) >5 µg/L, and radiomics features were independent predictors of overall survival (OS) and disease-free survival (DFS) in patients with lung ADC with an MIP component at stage IA. Incorporating this into the nomogram, the AUCs of the nomogram predicting 3-, 5-, and 7-year OS and DFS were 0.910, 0.914, and 0.904 and 0.868, 0.838, and 0.848, respectively, in the training cohort and 0.879, 0.895, and 0.899 and 0.817, 0.805, and 0.811, respectively, in the validation cohort, showing good differentiation. The OS and DFS survival analyses among different risk subgroups showed that the nomogram could well distinguish between low- and high-risk groups. We developed and validated a nomogram based on clinicopathological factors and radiomics features, which can be used as a powerful tool for predicting postoperative recurrence and survival in patients with stage IA lung ADC containing an MIP component.

Magnetic Resonance Imaging Radiomics Analysis of Noncystic Kidney Parenchyma to Differentiate among Mayo Imaging Classification Classes

Magnetic Resonance Imaging Radiomics Analysis of Noncystic Kidney Parenchyma to Differentiate among Mayo Imaging Classification Classes

Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy

BackgroundKidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.MethodsWe included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).ResultsOverall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75–0.99) and 0.74 (95% CI 0.52–0.93) for total parenchymal and cortical ROI features, respectively.ConclusionSignificant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.

Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors.

Radiomics models have been widely exploited in oncology for the investigation of tumor classification, as well as for predicting tumor response to treatment and genomic sequence; however, their performance in veterinary gastrointestinal tumors remains unexplored. Here, we sought to investigate and compare the performance of radiomics models in various settings for differentiating among canine small intestinal adenocarcinoma, lymphoma, and spindle cell sarcoma. Forty-two small intestinal tumors were contoured using four different segmentation methods: pre- or post-contrast, each with or without the inclusion of intraluminal gas. The mesenteric lymph nodes of pre- and post-contrast images were also contoured. The bin settings included bin count and bin width of 16, 32, 64, 128, and 256. Multinomial logistic regression, random forest, and support vector machine models were used to construct radiomics models. Using features from both primary tumors and lymph nodes showed significantly better performance than modeling using only the radiomics features of primary tumors, which indicated that the inclusion of mesenteric lymph nodes aids model performance. The support vector machine model exhibited significantly superior performance compared with the multinomial logistic regression and random forest models. Combining radiologic findings with radiomics features improved performance compared to using only radiomics features, highlighting the importance of radiologic findings in model building. A support vector machine model consisting of radiologic findings, primary tumors, and lymph node radiomics features with bin count 16 in post-contrast images with the exclusion of intraluminal gas showed the best performance among the various models tested. In conclusion, this study suggests that mesenteric lymph node segmentation and radiological findings should be integrated to build a potent radiomics model capable of differentiating among small intestinal tumors.

A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.

To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP). A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.

Value of 18F-FDG PET/CT-based radiomics features for differentiating primary lung cancer and solitary lung metastasis in patients with colorectal adenocarcinoma

Objective To investigate the value and applicability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in differentiating primary lung cancer (PLC) from solitary lung metastasis (SLM) in patients with colorectal cancer (CRC). Materials and methods This retrospective study included 103 patients with CRC and solitary pulmonary nodules (SPNs). The least absolute shrinkage and selection operator (LASSO) was used to screen for optimal radiomics features and establish a PET/CT radiomics model. PET/CT Visual and complex models (combining radiomics with PET/CT visual features) were developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the predictive value and diagnostic efficiency of the models. Results The AUC of the PET/CT radiomics model for differentiating PLC from SLM was 0.872 (95% CI: 0.806–0.939), which was not different from that of the visual (0.829 [95% CI: 0.749–0.908; p = .352]). However, the AUC of the complex model (0.936 [95% CI:0.892–0.981]) was significantly higher than that of the PET/CT radiomics (p = .005) and visual model (p = .001). The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of PET/CT radiomics for differentiating PLC from SLM were 0.720, 0.887, 0.806, 0.857, and 0.770, respectively. Conclusion PET/CT radiomics can effectively distinguish PLC and SLM in patients with CRC and SPNs and guide the implementation of personalized treatment.

Enhanced computed tomography radiomics predicts solute carrier family 7, member 11 in head and neck squamous cell carcinoma.

Traditional prognostic indicators for head and neck squamous cell carcinoma (HNSCC), such as clinicopathological features, human papillomavirus status, and imaging examinations, often lack precision in guiding medical therapy. Therefore, discovering novel tumor biomarkers that can accurately assess prognosis and aid in personalized medical treatment for HNSCC is critical. Solute carrier family 7, member 11 (SLC7A11), is implicated in ferroptosis, and various malignant tumor therapies regulate its expression. However, the mechanisms regulating SLC7A11 expression, the transporter activity, and its specific role in controlling ferroptosis in cancer cells remain unknown. Thus, in this study, we aimed to develop an improved computed tomography (CT) radiomics model that could predict SLC7A11 expression in patients with HNSCC. We used patient genomic data and corresponding augmented CT images for prognostic analysis and building models. Further, we investigated the potential molecular mechanisms underlying SLC7A11 expression in the immune microenvironment. Our radiomics model successfully predicted SLC7A11 mRNA expression in HNSCC tissues and elucidated its association with relevant genes and prognostic outcomes. SLC7A11 expression level was high within tumor tissues and was connected to the infiltration of eosinophil, CD8+ T-cell, and macrophages, which was associated with poor overall survival. Our models demonstrated robust predictive power. The distribution of radiomics scores (RAD scores) within the training and validation sets was markedly different between the high- and low-expression groups of SLC7A11. SLC7A11 is likely an important factor in the prognosis of HNSCC. SLC7A11 expression can be predicted effectively and reliably by radiomics models based on enhanced CT.

A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms

Objective To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB). Methods In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed. Results The Student’s t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models. Conclusion The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.

Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor

BackgroundThis study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.MethodsThis study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.ResultsThe single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.ConclusionsThe combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

BackgroundTo design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.MethodsThis retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models’ performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method.ResultsThe study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively.ConclusionsA radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.

Baseline 18F-FDG PET Radiomics Predicting Therapeutic Efficacy of Diffuse Large B-Cell Lymphoma after R-CHOP (-Like) Therapy.

Objective: This study aimed to predict therapeutic efficacy among diffuse large B-cell lymphoma (DLBCL) after R-CHOP (-like) therapy using baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) radiomics. Methods: A total of 239 patients with DLBCL were enrolled in this study, with 82 patients having refractory/relapsed disease. The radiomics signatures were developed using a stacking ensemble approach. The efficacy of the radiomics signatures, the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI), conventional PET parameters model, and their combinations in assessing refractory/relapse risk were evaluated using receiver operating characteristic (ROC) curves, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and decision curve analysis. Results: The stacking model, along with the integrated model that combines stacking with the NCCN-IPI and SDmax (the distance between the two lesions farthest apart, normalized to the patient's body surface area), showed remarkable predictive capabilities with a high area under the curve (AUC), sensitivity, specificity, PPV, NPV, accuracy, and significant net benefit of the AUC (NB-AUC). Although no significant differences were observed between the combined and stacking models in terms of the AUC in either the training cohort (AUC: 0.992 vs. 0.985, p = 0.139) or the testing cohort (AUC: 0.768 vs. 0.781, p = 0.668), the integrated model exhibited higher values for sensitivity, PPV, NPV, accuracy, and NB-AUC than the stacking model. Conclusion: Baseline PET radiomics could predict therapeutic efficacy in DLBCL after R-CHOP (-like) therapy, with improved predictive performance when incorporating clinical features and SDmax.