Pre-treatment CT Images Research Articles

Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study

BackgroundDespite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this...

Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion

BackgroundThis research aimed to develop a model for individualized treatment decision-making in inoperable elderly patients with esophageal squamous cell carcinoma (ESCC) using machine learning methods and multi-modal data.MethodsA total of 189 inoperable elderly ESCC patients aged 65 or older who underwent concurrent chemoradiotherapy (CCRT) or radiotherapy (RT) were included. Multi-task learning models were created using machine learning techniques to analyze multi-modal data, including pre-treatment CT images, clinical information, and blood test results. Nomograms were constructed to predict the objective response rate (ORR) and progression-free survival (PFS) for different treatment strategies. Optimal treatment plans were recommended based on the nomograms. Patients were stratified into high-risk and low-risk groups using the nomograms, and survival analysis was performed using Kaplan–Meier curves.ResultsThe identified risk factors influencing ORR were histologic grade (HG), T stage and three radiomic features including original shape elongation, first-order skewness and original shape flatness, while risk factors influencing PFS included BMI, HG and three radiomic features including high gray-level run emphasis, first-order minimum and first-order skewness. These risk factors were incorporated into the nomograms as independent predictive factors. PFS was substantially different between the low-risk group (total score ≤ 110) and the high-risk group (total score > 110) according to Kaplan–Meier curves (P < 0.05).ConclusionsThe developed predictive models for ORR and PFS in inoperable elderly ESCC patients provide valuable insights for predicting treatment efficacy and prognosis. The nomograms enable personalized treatment decision-making and can guide optimal treatment plans for inoperable elderly ESCC patients.

CT Based Tumor Radiomics with Machine Learning Classifiers for Molecular Subtyping of Diffuse Large B Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBL) has been classified into two distinct molecular subtypes based on their cell of origin- namely germinal center B-cell-like (GCB) and activated B-cell-subtype (ABC) based on Han's algorithm. These two entities are also prognostically distinct. We assessed CT based tumor radiomic features using machine learning classifiers to distinguish the two molecular subtypes. One hundred one patients were accrued in the study after institutional ethics committee approval. GCB subtype was diagnosed in 59 while 42 patients were ABC subtype. Lesion with maximum SUV was delineated using a semi-automated segmentation tool (3D slicer) and radiomic features were extracted using Pyradiomics. A total of 1030 features were extracted including Shape features, first, second, third, higher order and wavelet-based features. Recursive feature elimination was used for feature selection and optimal 10 features were selected. Five machine learning (ML) tools were used to build a model to sub- classify the two molecular subtypes. (Table -1) Model performance was assessed using Accuracy, Precision, Recall, F1 score and Area under curve of receiver operating characteristic curve. The models were constructed by two methods.1) A 5-fold internal cross validation strategy & 2) splitting the data into training (70%) and validation set (30%). Recursive feature elimination using a random forest algorithm method was used to select 7 radiomic features. These 7 features were then used to build the model using the 5 ML classifiers. Of the five classifiers, Random Forest Classifier (RFC) using a 70:30 training: test strategy was the best performer among all the ML models, with the highest accuracy of 80%, AUC of 0.87, recall(sensitivity), f1score(specificity) & precision (Positive Predictive value; PPV) or precision of 82% respectively. When an internal 5-fold cross validation strategy was used, RFC again performed better than other ML classifiers with an accuracy of 75%, AUC of 0.80, recall of 79%, PPV of 77% and specificity of 79%. The machine-learning based Radiomics features extracted from pre-treatment CT images can provide a simple and non-invasive method for the prediction of DLBL molecular subtypes with favorable predictive accuracy. The Random Forest Classifier was the most accurate in distinguishing GCB subtype from the ABC subtype.

Assessing Displacement of Plastic Interstitial Catheters in Image-Guided Gynecological HDR Brachytherapy

Interstitial HDR brachytherapy involves precise, localized delivery to targets with high dose gradients, sparing adjacent organs at risk. Due to the proximity of the rectum and bladder to the target, deviations in the catheters with respect to patient anatomy can change dose to those structures. While utilizing plastic interstitial catheters allows the patient to receive hyperthermia therapy during their course of interstitial treatment, the plastic catheters were observed to show significant deflection when the metal stylets are removed for treatment. The hyperthermia electrodes and the HDR source cable are both wires of approximately the same thickness. The purpose of this study is to assess the magnitude and deflection of the plastic catheters for gynecologic interstitial plans and determine whether using flexible wires for visualization on pre-treatment imaging is suitable for reconstructing the actual treatment received. Pre-treatment CT images where the stylets were utilized for catheter visualization were registered to a subsequent CT scan with the metal stylets removed. The original treatment dwell positions were adjusted to the second CT to evaluate 3D catheter displacement and dose distributions calculated for the bladder, rectum, and target. The plans were subsequently reconstructed on a pre-planning CT with flexible 20-gauge wires instead of metal stylets to determine their suitability as a surrogate for stylets. Absolute 3D displacement for all patients was 3.5 ± 5.4 mm (n = 3433 catheter dwell positions, mean ± SD, p < 0.001) comparing plans with the stylet in versus out. Absolute catheter deflection for interstitial treatments increased with the removal of the metal stylets with a difference of 0.75 ± 0.49° (n = 313 catheters, mean ± SD, p < 0.01). The plans utilizing flexible wire instead of stylets are observed to correlate with the plans with no stylets with absolute 3D displacement and angular deflection of 1.1 ± 0.6 mm and 0.35° ± 0.3° respectively (n = 39 catheters, mean ± SD, p > 0.2). While the average target EQD2 D90 reduced by 5% ± 5% by planning without the stylets, four patients would have experienced a deviation from the prescription by >10%. There was an overall decrease in bladder D2cc and overall increase in rectal D2cc in the plans with the stylets utilized vs removed. Bladder D2cc, rectal D2cc, and target coverage showed no appreciable difference between utilizing flexible wires and the plans without. The difference in HU between the metal stylets and flexible wires was negligible. Catheter reconstruction in interstitial gynecological treatments with CT imaging revealed significant changes in catheter positioning with respect to the target volume once the stylets are removed for treatment. Using flexible wires of similar thickness to the source cable allow for more accurate tracking during planning without distorting the patient's anatomy or the treatment images, preserving the integrity of the treatment plan.

Predictive Modeling of Radiation Pneumonitis Induced by Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer Using Radiomics and Clinical Features

The purpose of this study is to establish a prediction model for the development of grade 2 or higher radiation pneumonitis (RP) using radiomics analysis of pretreatment CT images, PET images, and dose distribution, in addition to clinical factors, in patients with locally advanced non-small cell lung cancer (NSCLC) treated with radical chemoradiotherapy. We retrospectively evaluated 128 cases of locally advanced NSCLC treated with radical radiotherapy at our institution from 2008 to 2021. Clinical factors included age, sex, performance status (PS), KL-6, smoking history, histological type, clinical stage, and total radiation dose. Radiomics analysis was performed by analyzing treatment planning CT images, PET images, and dose distribution, and Rad-score (Radiomics-score) was calculated for the extracted features using Lasso-Cox regression. Rad-score (Radiomics-score) was calculated by Lasso-Cox regression for the extracted features. Risk factors were selected by univariate/multivariate analysis of clinical factors and Rad-score. Three models for predicting RP were developed from the identified risk factors using Nomogram: Clinical, Rad-score, and Combined model. The model was evaluated using area under the curve (AUC) based on receiver operating characteristic (ROC) curves and concordance index (C-index). RP was evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. The cumulative incidence of Grade 2 or higher RP was evaluated using the Kaplan-Meier method. Of the 128 cases, grade 2 or higher RP was observed in 50 cases (39%). Regarding clinical factors, gender, smoking status, and histology were selected as significant predictors of RP. Lasso-Cox analysis of radiomics features selected 11 features from CT images, 7 features from PET images, and 16 features from dose distribution as predictors of RP, yielding a total of 34 factors. The combined model (C-index: 0.96, AUC: 0.92) showed the best discrimination performance compared to the clinical model (C-index: 0.73, AUC: 0.56) and the Rad-score model (C-index: 0.87, AUC: 0.92). Risk classification using the combined model showed that the 1-year cumulative incidence of grade 2 or higher RP was 65% in the high-risk group, significantly higher than 15% in the low-risk group (p<0.001). The combined model with Rad-score and clinical factors can predict grade 2 or higher RP in NSCLC patients with high accuracy.

Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.

To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic features, and patient demographic information in an integrated machine learning workflow. The MFC model comprised three key steps. (1) Extraction of 92 handcrafted radiomic features from the GTV segmented on pre-treatment CT images. (2) Extraction of 512 deep radiomic features from pre-trained U-Net encoder. (3) The extracted handcrafted radiomic features, deep radiomic features, along with 4 patient demographic information (i.e., gender, age, tumor volume, and Charlson comorbidity index), were concatenated as a multi-dimensional input to the classifiers for LR prediction. Two NSCLC patient cohorts from our institution were investigated: (1) the surgery cohort includes 83 patients with segmentectomy or wedge resection (7 LR), and (2) the SBRT cohort includes 84 patients with lung SBRT (9 LR). The MFC model was developed and evaluated independently for both cohorts, and was subsequently compared against the prediction models based on only handcrafted radiomic features (R models), patient demographic information (PI models), and deep learning modeling (DL models). ROC with AUC was adopted to evaluate model performance with leave-one-out cross-validation (LOOCV) and 100-fold Monte Carlo random validation (MCRV). The t-test was performed to identify the statistically significant differences. In LOOCV, the AUC range (surgery/SBRT) of the MFC model was 0.858-0.895/0.868-0.913, which was higher than the three other models: 0.356-0.480/0.322-0.650 for PI models, 0.559-0.618/0.639-0.682 for R models, and 0.809/0.843 for DL models. In 100-fold MCRV, the MFC model again showed the highest AUC results (surgery/SBRT): 0.742-0.825/0.888-0.920, which were significantly higher than PI models: 0.464-0.564/0.538-0.628, R models: 0.557-0.652/0.551-0.732, and DL models: 0.702/0.791. We successfully developed an MFC model that combines feature information from multiple sources for proof-of-concept prediction of LR in patients with surgical and SBRT early-stage NSCLC. Initial results suggested that incorporating pre-treatment patient information from multiple sources improves the ability to predict the risk of local failure.

A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma

PurposeIdentifying robust prognosis and treatment efficiency predictive biomarkers of hepatocellular carcinoma (HCC) is challenging. The purpose of this study is to develop a radiomics approach for predicting the overall survival (OS) based on pretreatment CT images and to explore the radiomic-associated key genes. MethodsPatients with pathologically or clinically proven HCC from three data sets were retrospectively included in this study. The institute internal data that received transarterial chemoembolization (TACE) treatment was used as the training set to construct the radiomics signature to predict OS by the least absolute shrinkage and selection operator COX (LASSO-COX) regression algorithms. The model was externally tested in 41 patients from The Cancer Genome Atlas (TCGA) with available CT images. Area under the receiver operating characteristics curve (AUC) and the log-rank test were used for survival analysis based on high versus low radiomics score. RNA sequencing data of TCGA and Gene Expression Omnibus (GEO) public database were used for gene expression analysis. ResultsA total of 752 patients were divided into the Radiomics cohort (n = 267), the TCGA cohort (n = 338) and GEO cohort (n = 147). The rad-score divided patients into high and low risk groups, with significant survival differences (P < 0.0001 and P = 0.0055) in the training and external test set. The AUC for 5 years’ OS were 0.730 and 0.695, respectively. Seven OS-related genes (SPP1, GJA5, GJA4, INMT, PDZD4, ALDOA and MAFG) were identified, all of which were related with TACE efficiency, except for MAFG (P greater than 0.05). ConclusionsCT-radiomics signature could effectively predict the prognosis and treatment response of HCC, which were also associated with the tumor microenvironment heterogeneity.

Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation.

This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients. A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively. A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732-0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613-0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728-0.881) in the testing set. This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization. This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment. • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models.

Development of CT-based radiomic model to predict 5-year progression-free survival (PFS) in locally advanced head and neck squamous cell carcinoma (LAHNSCC) treated with definitive chemoradiation.

6076 Background: Definitive chemoradiation is the current standard treatment in LAHNSCC. Prognostic biomarkers that enable to reliably predict LAHNSCC disease progression are needed to stratify the risk of progression to tailor treatment intensity in future clinical trials. Imaging biomarkers have emerged as promising alternatives to non-invasively predict patient outcomes. Aim: to identify an imaging signature to predict progression-free survival (PFS) in LAHNSCC. Methods: A single-center, retrospective, observational study was designed. Clinical data and pre-treatment CT images from LAHNSCC patients treated with definitive chemoradiation from 2014 to 2022 were collected. Manual tumor segmentation was performed slice by slice by a qualified image technician and supervised by a radiologist with 20+ years in HNSCC using the DICOM viewer of Quibim platform. A total of 108 radiomic features (shape and textures) were extracted from each region of interest. Feature reduction techniques were applied to select the characteristics used in the classification model. The primary endpoint of this study was 5-year PFS. Random Forest, Support Vector Classifier (SVC), Logistic Regression, Gradient Boosting (GB) and Extreme Gradient Boosting classification models were used. A 5-fold cross validation strategy was followed to evaluate the performance of the models in terms of AUROC, sensitivity, specificity, and accuracy. The importance of each feature in the model was measured by applying the Shapley Additive exPlanations (SHAP) method. Results: Baseline CT exams from 102 LAHNSCC patients were included; 50% and 23% were stage IVA and IVB, respectively. The remaining 27% (stage II [3%] and stage III [24%]) were treated in an organ-preservation intention. 67% of patients presented locoregional or distant progression at 5 years. All radiomic features (108) and 6 clinical variables were used in the predictive model, for which a cross-validation was performed. GB as the feature selection technique and SVC as the classification model provided the best results. The final model was able to predict 5-year PFS with an average AUC of 0.82 (95% CI: [0.73, 0.91]), a sensitivity of 0.69 (95% CI: [0.57, 0.81]), a specificity of 0.82 (95% CI: [0.7, 0.94]) and an accuracy of 0.73 (95% CI: [0.62, 0.84]). From most to least important, features contributing to the predictive model were: TNM stage (SHAP: 0.038), glszm Small Area Emphasis (SHAP: 0.026), glcm Idmn (SHAP: 0.023), glszm Large Area High Gray Level Emphasis (SHAP: 0.018) and glrlm Run Entropy (SHAP: 0.015). Conclusions: Radiomic biomarkers from pre-treatment CT images in combination with TNM stage were predictive of 5-year PFS to chemoradiation in LAHNSCC patients and might be helpful for patient risk stratification. Further validation of these imaging biomarkers is ongoing.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Prediction of hemorrhagic transformation via pre-treatment CT radiomics in acute ischemic stroke patients receiving endovascular therapy.

This study aimed to extract radiomics features (RFs) from pre-treatment CT scans in patients with acute ischemic stroke (AIS), and to establish a radiomics model to predict hemorrhagic transformation (HT) after endovascular therapy (EVT). A total of 105 patients who were diagnosed with AIS [with occlusion of the M1 segment of the middle cerebral artery (MCA) and/or internal carotid artery] and received EVT were enrolled. They were randomly divided into the development cohort (n = 73) and the validation cohort (n = 32). The clinicoradiological data of all patients, including pre-treatment cranial CT without contrast enhancement, CT perfusion, and CT angiography, were obtained. The MCA territory on pre-treatment CT images was segmented to extract RFs associated with HT after EVT. Then, a CT radiomics model based on the selected RFs was constructed to predict HT after EVT. The sensitivity, specificity, and area under the curve of the CT radiomics model for predicting HT after EVT based on pre-treatment CT RFs was 0.806, 0.649, and 0.781 (95% confidence interval (CI): 0.675-0.886), respectively, in the development cohort. The sensitivity, specificity, and area under the curve in the validation cohort was 0.625, 0.875, and 0.797 (95% CI: 0.642-0.951), respectively. CT radiomics analysis is a valuable tool for predicting HT in AIS patients receiving EVT. It may guide the selection of patients in practice and improve procedural safety and effectiveness. Identification of the importance of pre-treatment CT radiomics in the prediction of HT in AIS patients after EVT.

CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study

BackgroundStratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre-treatment CT images for predicting MMR status in CRC.Methods1812 eligible participants (training cohort: n = 1124; internal validation cohort: n = 482; external validation cohort: n = 206) with CRC were enrolled from two institutions. All pretherapeutic CT images from three dimensions were trained by the ResNet101, then integrated by Gaussian process regression (GPR) to develop a full-automatic DL model for MMR status prediction. The predictive performance of the DL model was evaluated using the area under the receiver operating characteristic curve (AUC) and then tested in the internal and external validation cohorts. Additionally, the participants from institution 1 were sub-grouped by various clinical factors for subgroup analysis, then the predictive performance of the DL model for identifying MMR status between participants in different groups were compared.ResultsThe full-automatic DL model was established in the training cohort to stratify the MMR status, which presented promising discriminative ability with the AUCs of 0.986 (95% CI 0.971–1.000) in the internal validation cohort and 0.915 (95% CI 0.870–0.960) in the external validation cohort. In addition, the subgroup analysis based on the thickness of CT images, clinical T and N stages, gender, the longest diameter, and the location of tumors revealed that the DL model showed similar satisfying prediction performance.ConclusionsThe DL model may potentially serve as a noninvasive tool to facilitate the pre-treatment individualized prediction of MMR status in patients with CRC, which could promote the personalized clinical-making decision.

Pretherapy investigations using highly robust visualized biomarkers from CT imaging by multiple machine-learning techniques toward its prognosis prediction for ALK-inhibitor therapy in NSCLC: a feasibility study.

Molecularly targeted therapy has revolutionized the therapeutic landscape and is emerging as the first-line treatment option for ALK-rearranged non-small-cell lung cancer (NSCLC). In this study, the highly informative and robust biomarkers based on pre-treatment CT images and clinicopathologic features will be developed and validated to predict the prognosis for ALK-inhibitor therapy in NSCLC patients. A total of 161 ALK-positive NSCLC patients treated with ALK inhibitors were retrospectively collected as training, validation and test sets from multi-center institutions. Cox proportional hazard regression (CPH) penalized by LASSO and random survival forest (RSF) coupled with recursive feature elimination (RFE) were used for radiomics and clinical features identification and model construction. An overlapping post-processing method was extra added to training process to investigate the stronger biomarker on the whole set. 123 of the collected cases progressed after a median follow-up of 15.5months (IQR, 8.3-25.3). The T and M staging, pericardial effusion, age and ALK inhibitor-alectinib were determined as significant predictors in the survival analysis. Furthermore, we visualized the finally retained 4 radiomics feature. The RSF models built from overlapping-processed clinical and radiomics features respectively reached the maximum C-index of 0.68 and 0.75,but the combination of them,radioclinical signature, improved the score to 0.78. The model on the validation and external test datasets yielded the C-index of 0.73 and 0.79, with the iAUC of 0.76 and 0.83, the IBS of 0.119 and 0.112. With respect to a simple selection strategy of overlapping optimal radiomics and clinical features from different survival models may promote better progression-free survival(PFS) prediction than conventional survival analysis, which provides a potential method for guiding personalized pre-treatment options of NSCLC.

Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status.

Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.

Abstract WMP80: Multimodal CT Imaging Of Ischemic Stroke Thrombi Identifies Scale-Invariant Radiomic Features

Background: Paired analysis of ischemic stroke clot histology and pre-treatment CT imaging could identify digital indicators of treatment outcome or stroke etiology. Yet, differences in scale limit our ability to interpret the biology of the clot from pre-treatment images. Hypothesis: Radiomic analysis of resected clots on microCT has the potential to identify important, biologically-interpretable features that are correlated among pre-treatment imaging modalities (CTA, nCCT) and post-treatment histopathology. Methods: We performed multimodal CT imaging of 10 stroke clots retrieved by mechanical thrombectomy (example in top of Figure). Clots were manually segmented from co-registered, pre-treatment CTA and nCCT images. For the same cases, portions of resected clots were stained with iodine, imaged on a ScanCo microCT 100 (4.9 μm resolution), and segmented using adaptive intensity thresholding. Radiomic texture features ( n =93 for each modality, 279 total) were extracted from the images using PyRadiomics. Pearson correlation analysis was used to test associations between CTA (or nCCT) and microCT radiomics. Statistical significance was considered at R ≥0.7 and q &lt;0.05, after multiple testing correction. Results: 18 features were significantly correlated between CTA and microCT, and 5 features were significantly correlated between nCCT and microCT (bottom of Figure). Cluster Prominence ( R =0.96, q &lt;0.001) and Cluster Shade ( R =0.92, q &lt;0.001) on CTA and microCT exhibited near-perfect positive correlation. When comparing microCT and nCCT, FOS Energy ( R =0.90, q &lt;0.001) and Total Energy ( R =0.90, q &lt;0.001) had high positive correlation. Our findings suggest that RBC density (reflected in brighter pixels) and heterogeneity of clot constituents (reflected in pixel intensity) are scale-invariant clot attributes detectable on CT. Conclusion: Scale-invariant radiomic features can help bridge the gap between pre-treatment imaging and clot pathobiology.

Development and external validation of a non-invasive imaging biomarker to estimate the microsatellite instability status of gastric cancer and its prognostic value: The combination of clinical and quantitative CT-imaging features

PurposeMolecular testing for microsatellite instability (MSI) status plays a vital role in the clinical management of gastric cancer (GC). Nevertheless, challenges of routinely applied technology for MSI determination exist. This study aimed to develop and validate a non-invasive imaging biomarker for MSI assessment in GC and explore its prognostic value. MethodsWe retrospectively recruited 396 GC patients with pretreatment CT images from a single center and a public database and divided them into an original cohort (n = 356) and an external validation cohort (n = 40). The SMOTE algorithm was used to generate a balanced training cohort (n = 192) and the independent radiomics model, clinical model, and radiomics-clinic combined model were constructed for determining MSI status. The models’ discrimination, calibration, clinical usefulness, and prognosis significance were evaluated by AUC, calibration, decision curve analyses, and Kaplan-Meier curve analysis, respectively. ResultsThe radiomics-clinic combined model derived from clinical and quantitative CT-based “Radscore” exhibited the best discriminatory abilities of MSI status in all cohorts, with AUCs of 0.836 (95% CI, 0.780–0.893) in the training cohort, 0.834 (95% CI, 0.688–0.981) in the external validation cohort, and 0.750 (95% CI, 0.682–0.819) in the original cohort, respectively. Meanwhile, the combined model demonstrated goodness of fitness, higher clinical net benefits, and significant positive integrated discrimination improvement compared with any independent model. While it showed no significant overall survival- or progression-free survival-based risk stratification ability (p > 0.05). ConclusionsThe radiomics-clinic combined model could be a potential non-invasive biomarker for MSI status in GC, which help clinical decision-making, nevertheless, provided limited prognostic ability.

Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy

Purpose: to assess the likelihood of local recurrence of lung malignancies following stereotactic ablative radiotherapy (SABR) by evaluating clinical and radiomic features with machine learning and novel use of deep learning methods.&#x0D; Methods: pre-treatment CT images were attained from seventy patients with primary lung malignancies. The malignancy was segmented by the treating radiation oncologist and 107 radiomic features were extracted from the image. The data underwent feature reduction via Spearman’s correlation and selection with adapted LASSO regression analysis. A random forest model and a multilayer perceptron (MLP) with cost-sensitive classifier were independently used to assess for local recurrence of malignancy. The recurrence likelihood predictions from each of these were used to stratify patients into groups with high and low risk of recurrence. These were assessed for time-to-event predictions using Kaplan-Meier analyses and Gray’s test to evaluate the separation between the high and low-risk groups. The prognostic capacity of the models was evaluated with a concordance index, 95% confidence intervals and bootstrapping (10,000 iterations).&#x0D; Results: the MLP was able to predict the recurrence of malignancy with 100% sensitivity and 91% specificity (AUC 0.95). The MLP predictions showed statistically significant separation of high and low-risk patients, and robust model fit (p=0.04, c=0.79), which out-performed random forest model predictions (p=0.15, c=0.41) that did not reach statistical significance.&#x0D; Conclusions: radiomic data analysis with an MLP showed improved prediction potential within this dataset compared to random forest models for predicting local recurrence of lung cancer.

A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer.

To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75-0.82) compared with the largest lesion approach (AUCs: 0.70-0.78) and the average sum approach (AUCs: 0.64-0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84-0.89], 0.75 [0.68-0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89-0.93], p = .042; validation: AUC 0.86 [0.80-0.91], p = .011; test: AUC 0.86 [0.76-0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40-2.95, p < 0.05). The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer.

Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers

BackgroundImmune checkpoint inhibitors (ICIs) represent an approved treatment for various cancers; however, only a small proportion of the population is responsive to such treatment. We aimed to develop and validate a plain CT-based tool for predicting the response to ICI treatment among cancer patients.MethodsData for patients with solid cancers treated with ICIs at two centers from October 2019 to October 2021 were randomly divided into training and validation sets. Radiomic features were extracted from pretreatment CT images of the tumor of interest. After feature selection, a radiomics signature was constructed based on the least absolute shrinkage and selection operator regression model, and the signature and clinical factors were incorporated into a radiomics nomogram. Model performance was evaluated using the training and validation sets. The Kaplan–Meier method was used to visualize associations with survival.ResultsData for 122 and 30 patients were included in the training and validation sets, respectively. Both the radiomics signature (radscore) and nomogram exhibited good discrimination of response status, with areas under the curve (AUC) of 0.790 and 0.814 for the training set and 0.831 and 0.847 for the validation set, respectively. The calibration evaluation indicated goodness-of-fit for both models, while the decision curves indicated that clinical application was favorable. Both models were associated with the overall survival of patients in the validation set.ConclusionsWe developed a radiomics model for early prediction of the response to ICI treatment. This model may aid in identifying the patients most likely to benefit from immunotherapy.

PET/CT radiomics for prediction of hyperprogression in metastatic melanoma patients treated with immune checkpoint inhibitors.

This study evaluated pretreatment 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET/CT-based radiomic signatures for prediction of hyperprogression in metastatic melanoma patients treated with immune checkpoint inhibition (ICI). Fifty-six consecutive metastatic melanoma patients treated with ICI and available imaging were included in the study and 330 metastatic lesions were individually, fully segmented on pre-treatment CT and FDG-PET imaging. Lesion hyperprogression (HPL) was defined as lesion progression according to RECIST 1.1 and doubling of tumor growth rate. Patient hyperprogression (PD-HPD) was defined as progressive disease (PD) according to RECIST 1.1 and presence of at least one HPL. Patient survival was evaluated with Kaplan-Meier curves. Mortality risk of PD-HPD status was assessed by estimation of hazard ratio (HR). Furthermore, we assessed with Fisher test and Mann-Whitney U test if demographic or treatment parameters were different between PD-HPD and the remaining patients. Pre-treatment PET/CT-based radiomic signatures were used to build models predicting HPL at three months after start of treatment. The models were internally validated with nested cross-validation. The performance metric was the area under receiver operating characteristic curve (AUC). PD-HPD patients constituted 57.1% of all PD patients. PD-HPD was negatively related to patient overall survival with HR=8.52 (95%CI 3.47-20.94). Sixty-nine lesions (20.9%) were identified as progressing at 3 months. Twenty-nine of these lesions were classified as hyperprogressive, thereby showing a HPL rate of 8.8%. CT-based, PET-based, and PET/CT-based models predicting HPL at three months after the start of treatment achieved testing AUC of 0.703 +/- 0.054, 0.516 +/- 0.061, and 0.704 +/- 0.070, respectively. The best performing models relied mostly on CT-based histogram features. FDG-PET/CT-based radiomic signatures yield potential for pretreatment prediction of lesion hyperprogression, which may contribute to reducing the risk of delayed treatment adaptation in metastatic melanoma patients treated with ICI.