Quantitative Imaging Features Research Articles

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy.

Simple SummaryIn this study, we investigated the longitudinal changes and predictive value of multiparametric MRI (mpMRI) features of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) boost radiotherapy (RT). Ninety-five mpMRI from 25 patients, acquired pre-RT and at 3 time points following RT were analyzed. Five regions of interest were analyzed, related to tumor, peritumoral, and normally-appearing tissues. We identified significant changes in the imaging parameters following RT in all regions. By using selected features at the four scan points and their differences (Δ radiomics), we were able to build viable predictive models for endpoint biopsy positivity. Our study demonstrates that RT causes significant changes to quantitative imaging features in both tumorous and normally-appearing tissues. Because of the noninvasive nature of the mpMRI, acquiring multiple exams post-RT is feasible to monitor treatment response. Several quantitative imaging features are promising predictors of treatment failure based on post-treatment positive biopsy, a strong marker for clinically relevant endpoints.We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances.

Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

Utility of 99m Technetium Pertechnetate Thyroid Scan and Uptake in Thyrotoxic Patients: Jordanian Experience.

Objective The objective of this study was to assess our local experience with 99m Technitium thyroid uptake (TcTU) in thyrotoxicosis by examining mean and range of TcTU in both euthyroid patients and thyrotoxic patients. We also wanted to see how well TcTU performed as a substitute for radioiodine thyroid uptake in thyrotoxicosis. Methods The medical records of thyrotoxic patients were reviewed retrospectively. Inclusion criteria were: (1) thyrotoxicosis was proven biochemically, (2) the patient underwent TcTU at the time of thyrotoxicosis diagnosis, (3) at least 6 months of follow-up, and (4) the final diagnosis was documented. All TcTU of euthyroid patients were also reviewed to determine local normal TcTU mean and range. Patients were divided into three groups: Graves' disease, toxic nodular goiter, and subacute thyroiditis. Each patient group's TcTU mean and range were assessed separately. Results There were 209 patients in total (54 euthyroid, 112 Graves' disease, 26 toxic nodules, and 17 patients with subacute thyroiditis). TcTU mean±standard deviation and range for euthyroid patients were 1.5±1.1% and 0.17 to 4.8%, 10.6±10% and 0.43 to 40% for Graves' disease, 4.5±4% and 0.6 to 15% for toxic nodules, and 0.5±0.4% and 0.18 to 1% for subacute thyroiditis. Although one-third of thyrotoxic patients' TcTU values overlapped with the normal TcTU range, the diagnosis was made using qualitative image analysis. Subacute thyroiditis was characterized by poor thyroid visualization, whereas Graves'/toxic nodular goiter was well visualized. Conclusion The mean and range of our local normal TcTU were similar to those previously published. TcTU was a useful alternative to radioiodine in the evaluation of thyrotoxicosis. About two-thirds of the patients had accurate test results. The diagnosis was reached in the remaining one-third of patients by combining quantitative and qualitative image features. This method allowed us to stop giving radioiodine to our patients, resulting in a significant reduction in patient radiation exposure.

1239P Radiomics biomarkers are associated with survival in patients with oesophageal adenocarcinoma

The five-year overall survival for oesophageal adenocarcinoma remains poor at approximately 15%-25%. This highlights the need for improved therapeutic approaches. Radiomics studies combine quantitative medical image features with genomics to assess tumour phenotype as a “virtual biopsy.” These have shown promising results defining medical image features that predict treatment response and survival in oesophageal squamous cell carcinoma. Currently, there are very few published studies examining radiomics profiles in OAC.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning

BackgroundInfiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration.MethodsA total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit the predictive model based on infiltration features. Furthermore, the receiver operating characteristic curve (ROC) was generated, and the diagnostic performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, precision, recall, and F1 value.ResultsA variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm, and 19 optimal features were finally selected. The SVM models for predicting high infiltration yielded an AUC of 0.86 (sensitivity: 0.81, specificity 0.79) in the training set and 0.73 (sensitivity: 0.87, specificity: 0.80) in the validation set. The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set (accuracy: 0.80, precision: 0.82, recall: 0.81, F1 score: 0.81) and independent verification set (accuracy: 0.85, precision: 0.93, recall: 0.87, F1 score: 0.90).ConclusionsThe radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration. This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies.

Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

Simple SummaryGlioblastoma (GBM) is a cancer with poor prognosis, its 5-year survival expectation is approximately 5%. Advances in oncologic treatment techniques have not led to significant improvements in survival outcomes for GBM patients. Part of the reason for the treatment failures in GBM patients is that treatments fail to account for heterogeneities both within and between different tumors. Radiomics is a rapidly emerging research field that examines the relationship between medical imaging features and patient clinical outcomes and biological characteristics of tumours. This review outlines the applications of radiomics for GBM patient management and the barriers facing the implementation of radiomics into clinical practice. In completing this review, we hope to inform clinicians and researchers on how radiomics may be used to improve patient clinical outcomes.Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the “University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics” (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1

Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Prognostic Evaluation Based on Dual-Time 18F-FDG PET/CT Radiomics Features in Patients with Locally Advanced Pancreatic Cancer Treated by Stereotactic Body Radiation Therapy.

Background 18F-FDG PET/CT is widely used in the prognosis evaluation of tumor patients. The radiomics features can provide additional information for clinical prognostic assessment. Purpose Purpose is to explore the prognostic value of radiomics features from dual-time 18F-FDG PET/CT images for locally advanced pancreatic cancer (LAPC) patients treated with stereotactic body radiation therapy (SBRT). Materials and Methods This retrospective study included 70 LAPC patients who received early and delayed 18F-FDG PET/CT scans before SBRT treatment. A total of 1188 quantitative imaging features were extracted from dual-time PET/CT images. To avoid overfitting, the univariate analysis and elastic net were used to obtain a sparse set of image features that were applied to develop a radiomics score (Rad-score). Then, the Harrell consistency index (C-index) was used to evaluate the prognosis model. Results The Rad-score from dual-time images contains six features, including intensity histogram, morphological, and texture features. In the validation cohort, the univariate analysis showed that the Rad-score was the independent prognostic factor (p < 0.001, hazard ratio [HR]: 3.2). And in the multivariate analysis, the Rad-score was the only prognostic factor (p < 0.01, HR: 4.1) that was significantly associated with the overall survival (OS) of patients. In addition, according to cross-validation, the C-index of the prognosis model based on the Rad-score from dual-time images is better than the early and delayed images (0.720 vs. 0.683 vs. 0.583). Conclusion The Rad-score based on dual-time 18F-FDG PET/CT images is a promising noninvasive method with better prognostic value.

Combined whole-lesion radiomic and iodine analysis for differentiation of pulmonary tumors

Quantitative radiomic and iodine imaging features have been explored for diagnosis and characterization of tumors. In this work, we invistigate combined whole-lesion radiomic and iodine analysis for the differentiation of pulmonary tumors on contrast-enhanced dual-energy CT (DECT) chest images. 100 biopsy-proven solid lung lesions on contrast-enhanced DECT chest exams within 3 months of histopathologic sampling were identified. Lesions were volumetrically segmented using open-source software. Lesion segmentations and iodine density volumes were loaded into a radiomics prototype for quantitative analysis. Univariate analysis was performed to determine differences in volumetric iodine concentration (mean, median, maximum, minimum, 10th percentile, 90th percentile) and first and higher order radiomic features (n = 1212) between pulmonary tumors. Analyses were performed using a 2-sample t test, and filtered for false discoveries using Benjamini–Hochberg method. 100 individuals (mean age 65 ± 13 years; 59 women) with 64 primary and 36 metastatic lung lesions were included. Only one iodine concentration parameter, absolute minimum iodine, significantly differed between primary and metastatic pulmonary tumors (FDR-adjusted p = 0.015, AUC 0.69). 310 (FDR-adjusted p = 0.0008 to p = 0.0491) radiomic features differed between primary and metastatic lung tumors. Of these, 21 features achieved AUC ≥ 0.75. In subset analyses of lesions imaged by non-CTPA protocol (n = 72), 191 features significantly differed between primary and metastatic tumors, 19 of which achieved AUC ≥ 0.75. In subset analysis of tumors without history of prior treatment (n = 59), 40 features significantly differed between primary and metastatic tumors, 11 of which achieved AUC ≥ 0.75. Volumetric radiomic analysis provides differentiating capability beyond iodine quantification. While a high number of radiomic features differentiated primary versus metastatic pulmonary tumors, fewer features demonstrated good individual discriminatory utility.

ML-Based Texture and Wavelet Features Extraction Technique to Predict Gastric Mesothelioma Cancer.

Microsatellites are small, repetitive sequences found all across the human genome. Microsatellite instability is the phenomenon of variations in the length of microsatellites induced by the insertion or deletion of repeat units in tumor tissue (MSI). MSI-type stomach malignancy has distinct genetic phenotypes and clinic pathological characteristics, and the stability of microsatellites influences whether or not patients with gastric mesothelioma react to immunotherapy. As a result, determining MSI status prior to surgery is critical for developing treatment options for individuals with gastric cancer. Traditional MSI detection approaches need immunological histochemistry and genetic analysis, which adds to the expense and makes it difficult to apply to every patient in clinical practice. In this study, to predict the MSI status of gastric cancer patients, researchers used image feature extraction technology and a machine learning algorithm to evaluate high-resolution histopathology pictures of patients. 279 cases of raw data were obtained from the TCGA database, 442 samples were obtained after preprocessing and upsampling, and 445 quantitative image features, including first-order statistics of impressions, texture features, and wavelet features, were extracted from the histopathological images of each sample. To filter the characteristics and provide a prediction label (risk score) for MSI status of gastric cancer, Lasso regression was utilized. The predictive label's classification performance was evaluated using a logistic classification model, which was then coupled with the clinical data of each patient to create a customized nomogram for MSI status prediction using multivariate analysis.

Abstract CT109: Radiomic features in tumor assessment, preliminary results from a phase 2 study of intratumoral administration of BO-112 with pembrolizumab in patients with anti PD1 refractory melanoma

Abstract Background: There is a lack of predictive biomarkers for immunotherapy in melanoma. Immunohistochemistry and gene sequencing are frequently assessed as part of clinical research. Radiomic signatures may also add valuable information, based on parameters which can be related to immune infiltration, therefore defining an imaging biomarkers panel for this clinical scenario. BO-112 is a double stranded synthetic RNA formulated with polyethyleneimine (PEI) that, by mimicking the effect of a viral infection, mobilizes the immune system. The role of imaging biomarkers is being explored in the present phase II clinical trial. Study design: Single arm study with BO-112 plus pembrolizumab (NCT04570332) in patients with advanced melanoma in progression to anti-PD1 therapy. As part of exploratory objectives, a radiomics analysis was performed to detect changes in lesion texture features. Quantitative features were obtained by using Quibim Precision 2.9 platform (Quibim, Valencia, Spain) after the manual delineation of lesions on the CT study of each subject at each timepoint to obtain information about injected/non-injected lesions. Images were normalized by taking into account Hounsfield Units (HU) bias across scanners in a cross-calibration phantom and the Z-score. The difference (Delta) in the features between baseline and week 8 was calculated. Results: Studies were assessed based on event (progression) and based on whether lesions had been injected. Patients with only cutaneous disease were not included in this analysis. Out of 23 patients who had at least two imaging assessments, 6 developed progressive disease by week 8, and 17 subjects had no event by that time. Due to the small sample size, the radiomic analysis was based on hypothesis testing. With regards to volume, 50% of the non-progressing lesions reduced their value from that of the baseline. Regarding injected versus non-injected changes, up to 50% of injected lesions decreased their volume after 8 weeks of injected treatment whereas in non-injected lesions volume decreased in less than 25% of lesions. From the independent sample t-test of delta radiomics features against the injected/non-injected lesions variable, there were 4 features with a statistically significant difference between groups; all of them related to the Low Grey-Level Zone Emphasis. Specifically, Delta original GLRLM Low Grey-Level Run Emphasis showed the highest significant difference between injected and non-injected lesions (p=0.004), with higher and positive delta values in the injected group (75% injected lesions were above 0). Conclusions: Imaging biomarkers provide a large number of quantitative image features with a wide span of information, from size to heterogeneity of the tissue which may be indicator of tumor progression and immune infiltrate. In the analysis of radiomics features, delta GLRLM low grey-level zone emphasis was sensitive to the tumoral changes happening in injected lesions at week 8. This might add insights into the imaging-based evaluation of immune infiltration in intratumoral immunotherapy and the creation of associated imaging biomarkers panels. Citation Format: Paula Moreno, Philippe Saiag, Luis de la Cruz Merino, Caroline Dutriaux, Eduardo Castanon Álvarez, Caroline Robert, Juan F. Rodríguez-Moreno, Pablo Cerezuela-Fuentes, Ana M. Arance, Henry Montaudié, Miguel F. Sanmamed, María González Cao, María Pilar López Criado, Julie Charles, Alfonso Berrocal, Enrique de Miguel, Elisa Funk-Brentano, Pablo Sau Llanas, Sorilla Prey, Eva Muñoz-Couselo, Delvys Rodríguez Abreu, Juan Martin-Liberal, Ángel Alberich-Bayarri, Javier Sánchez López, Sonia Macia, Marisol Quintero, Marya F. Chaney, Stéphane Dalle, Iván Márquez-Rodas. Radiomic features in tumor assessment, preliminary results from a phase 2 study of intratumoral administration of BO-112 with pembrolizumab in patients with anti PD1 refractory melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT109.

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case.

Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.

ObjectiveThis study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention.MethodsOur study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’.ResultsThe image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset.ConclusionThe deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.

Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach

The commonly used magnetic resonance (MRI) criteria can be insufficient for discriminating mucinous from non-mucinous pancreatic cystic lesions (PCLs). The histological differences between PCLs’ fluid composition may be reflected in MRI images, but cannot be assessed by visual evaluation alone. We investigate whether additional MRI quantitative parameters such as signal intensity measurements (SIMs) and radiomics texture analysis (TA) can aid the differentiation between mucinous and non-mucinous PCLs. Fifty-nine PCLs (mucinous, n = 24; non-mucinous, n = 35) are retrospectively included. The SIMs were performed by two radiologists on T2 and diffusion-weighted images (T2WI and DWI) and apparent diffusion coefficient (ADC) maps. A total of 550 radiomic features were extracted from the T2WI and ADC maps of every lesion. The SIMs and TA features were compared between entities using univariate, receiver-operating, and multivariate analysis. The SIM analysis showed no statistically significant differences between the two groups (p = 0.69, 0.21–0.43, and 0.98 for T2, DWI, and ADC, respectively). Mucinous and non-mucinous PLCs were successfully discriminated by both T2-based (83.2–100% sensitivity and 69.3–96.2% specificity) and ADC-based (40–85% sensitivity and 60–96.67% specificity) radiomic features. SIMs cannot reliably discriminate between PCLs. Radiomics have the potential to augment the common MRI diagnosis of PLCs by providing quantitative and reproducible imaging features, but validation is required by further studies.

Radiomic features quantifying pixel-level characteristics of breast tumors from magnetic resonance imaging predict risk factors in triple-negative breast cancer.

e12612 Background: Computationally derived quantitative imaging (radiomic) features that describe tumor phenotypes at the pixel level have demonstrated associations with clinical characteristics in early investigations of other cancers. This implies that molecular differences among tumors may be reflected in their structure on the scales probed by 3D magnetic resonance imaging (MRI). We investigated whether radiomic features computed over tumor volumes from pre-treatment breast MRI could predict risk factors in triple-negative breast cancer (TNBC). Methods: We evaluated breast tumors on pre-treatment, post-contrast T1-weighted MRI from 156 patients with non-metastatic TNBC who underwent neoadjuvant chemotherapy. Tumor regions of interest were segmented by a convolutional neural network algorithm, with validation by breast radiologists. Features quantifying tumor shape and texture were extracted for the largest tumor present in each patient. We identified 23 principal components (PCs) describing these data within the original 364-dimensional feature space for further analysis. Tumor volume was also extracted for comparison with the shape and texture PCs, clinical variables and outcomes, but was kept separate from other radiomic features, since it directly correlates with clinical stage. We compiled for the cohort clinical variables including demographics, stage, grade, and, where available, absolute lymphocyte count (ALC) and Ki-67, a cellular proliferation index routinely used in clinical practice. We then performed a series of univariate and multivariate regression analyses to identify radiomic PCs and clinical variables that significantly predict patient outcomes, and radiomic PCs that predict established risk factors. Our multivariate analyses utilized 5-fold cross-validation and Monte-Carlo determination of p-values (based on 3000 random samplings from the null hypothesis), to ensure statistical rigor in identifying predictive relationships while correcting for multiple hypothesis testing. Results: Our univariate analyses confirmed expected correlations between: overall survival and pre-treatment tumor volume (p = 0.010); survival and ALC (p = 0.002); and clinical stage and tumor volume (p = 1.2⨉10-7). From our multivariate analysis, shape and texture radiomic features were predictive of: tumor volume (p &lt; 0.001); clinical stage (p &lt; 0.001); and Ki-67 (p = 0.02). We confirmed that Ki-67 was predictive of post-treatment residual cancer (p = 0.014), as has been previously reported. Conclusions: Radiomic features predict breast cancer risk factors that are significant for determining outcomes for TNBC patients. Combinations of radiomic shape and texture features track closely with tumor volumes, stage, and proliferative activity, potentially reflecting underlying molecular evolution.

Tumor-infiltrating lymphocyte enrichment predicted by CT radiomic analysis is associated with clinical outcomes of immune checkpoint inhibitor in non–small cell lung cancer.

2663 Background: Enrichment of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment (TME) is a reliable biomarker of immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Phenotyping through CT radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we objectively assess TIL enrichment using an artificial intelligence-powered H&amp;E analyzer, Lunit SCOPE IO, and analyze its association with advanced quantitative imaging features extracted via radiomic analysis. Clinical significance of the selected radiomic features (RFs) is then validated in independent NSCLC patients who received ICI. Methods: In the training cohort, which included 235 NSCLC patients with both tumor tissue and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. From tissue, a patient’s TIL enrichment score (TILes) was defined as the fraction of tissue area with high intra-tumoral or stromal TIL density, divided by the whole TME area, as measured on an H&amp;E slide. From the corresponding CT images, the least absolute shrinkage and selection operator model was then developed using features that were significantly associated with TIL enrichment. The CT model was then applied to CT images from the validation cohort, which included 242 NSCLC patients who received ICI as ≥ second line. Results: Among the extracted RFs, 22 features were significantly associated with TILes (p &lt; 0.005). After excluding features of multicollinearity and/or zero-coefficient, two features, gray level variance (coefficient 1.71 x 10-3) and low gray level emphasis (coefficient -2.48 x 10-5), were finally included in the model. The two features were both computed from the size-zone matrix (SZM), the idea of which is to break down a given tumor volume into smaller spatially contiguous compartments of different sizes. In the validation cohort, the patients with high predicted TILes (≥ median) had significantly prolonged progression-free survival compared with those with low predicted TILes (median 3.81 months [95% CI 2.14 – 5.69] versus 1.94 months [95% CI 1.58 – 2.93], hazard ratio 0.69 [95% CI 0.53 – 0.90], p = 0.007). Conclusions: This CT radiomics model is able to assess TIL enrichment in TME, which is significantly associated with favorable ICI outcomes in NSCLC. Analyzing the TME through radiomics may overcome limitations of tissue-based analysis and inform clinical decisions, particularly related to use of ICI.

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics.

Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

Quantitative and qualitative liver CT: imaging feature association with histopathologically confirmed hepatic cirrhosis

To assess the diagnostic performance of quantitative and qualitative imaging features of hepatic cirrhosis on CT. A single-center retrospective cohort study was performed on all patients who had undergone non-targeted liver biopsy < 3months following abdominal CT imaging between 2007 and 2020. Histopathology was required as a reference standard for hepatic cirrhosis diagnosis. Two readers independently assessed all CT quantitative and qualitative features, blinded to the clinical history and the reference standard. The diagnostic performance of each imaging feature was assessed using multivariate regression and logistic regression in a recursive feature elimination framework. 98 consecutive patients met inclusion criteria including 26 with histopathologically confirmed hepatic cirrhosis, and 72 without cirrhosis. Liver surface nodularity (p < 0.0001), lobar redistribution (p < 0.0001), and expanded gallbladder fossa (p < 0.0016) were qualitative CT features associated with liver cirrhosis consistent between both reviewers. Liver surface nodularity demonstrated highest sensitivity (73-77%) and specificity (79-82%). Falciform space width was the only quantitative feature associated with cirrhosis, for a single reviewer (p < 0.04). Using a recursive feature elimination framework, liver surface nodularity and falciform space width were the strongest performing features for identifying cirrhosis. No feature combinations strengthened diagnostic performance. Many quantitative and qualitative CT imaging signs of hepatic cirrhosis have either poor accuracy or poor inter-observer agreement. Qualitative imaging features of hepatic cirrhosis on CT performed better than quantitative metrics, with liver surface nodularity the most optimal feature for diagnosing hepatic cirrhosis.