Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening

Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging

OBJECTIVES

To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images.

METHODS

Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models were trained with 397 patients and validated with 170 internal and 31 external patients.

RESULTS

For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively.

CONCLUSIONS

Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

Objectives

To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).

Methods

A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed.

Results

A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts.

Conclusion

A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma

RATIONALE AND OBJECTIVES

Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning radiomics (DLR) model to accurately predict the response to NAC in patients diagnosed with OS using preoperative MR images.

METHODS

We reviewed axial T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted (T1CE) of 106 patients pathologically confirmed as OS. First, the Auto3DSeg framework was utilized for automated OS segmentation. Second, using three feature extraction methods, nine risk classification models were constructed based on three classifiers. The area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, negative predictive value and positive predictive value were calculated for performance evaluation. Additionally, we developed a deep learning radiomics nomogram with clinical indicators.

RESULTS

The model for OS automatic segmentation achieved a Dice coefficient of 0.868 across datasets. To predict the response to NAC, the DLR model achieved the highest prediction performance with an accuracy of 93.8% and an AUC of 0.961 in the test sets. We used calibration curves to assess the predictive ability of the models and performed decision curve analysis to evaluate the clinical net benefit of the DLR model.

CONCLUSION

The DLR model can serve as a pragmatic prediction tool, capable of identifying patients with poor response to NAC, providing information for risk counseling, and assisting in making clinical treatment decisions. Poor responders are better advised to undergo immunotherapy and receive the best supportive care.

A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer

OBJECTIVE

Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans.

METHODS

Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set.

RESULTS

Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749.

CONCLUSION

Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.

Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study

PURPOSE

Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS).

MATERIALS AND METHODS

Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion.

RESULTS

A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction.

CONCLUSION

Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.

Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer

Aim

To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM).

Methods

From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves.

Results

Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5-103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88.

Conclusions

MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.

The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition's benefits, we compared the predictivity of models relying on both "standard" and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.

Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review

OBJECTIVES

To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies.

METHODS

This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies.

RESULTS

Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall.

CONCLUSION

The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.

Peritumoral Radiomics for Identification of Telomerase Reverse Transcriptase Promoter Mutation in Patients With Glioblastoma Based on Preoperative MRI

Purpose: To evaluate the value of intra- and peritumoral deep learning (DL) features based on multi-parametric magnetic resonance imaging (MRI) for identifying telomerase reverse transcriptase (TERT) promoter mutation in glioblastoma (GBM). Methods: In this study, we included 229 patients with GBM who underwent preoperative MRI in two hospitals between November 2016 and September 2022. We used four 2D Convolutional Neural Networks (GoogLeNet, DenseNet121, VGG16, and MobileNetV3-Large) to extract intra- and peritumoral DL features. The Mann-Whitney U test, Pearson correlation analysis, least absolute shrinkage and selection operator, and logistic regression analysis were used for feature selection and construction of DL radiomics (DLR) signatures in different regions. These multi-parametric and multi-region signatures were combined to identify TERT promoter mutation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effects of the signatures. Results: The signatures based on the DL features from the peritumoral regions with expansion distances of 2 mm, 8 mm, and 10 mm using the GoogLeNet architecture correlated with the optimal AUC values (test set: .823, .753, and .768) in the T2-weighted, T1-weighted contrast-enhanced, and T1-weighted images. Using the stacking fusion method, DLR with multi-parameter and multi-region fusion achieved the best discrimination with AUC values of .948 and .902 in the training and test sets, respectively. Conclusions: The radiomics model based on the fusion of multi-parameter MRI intra- and peritumoral DLR signatures may help to identify TERT promoter mutation in patients with GBM.

State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation

Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.

Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy

BACKGROUND AND PURPOSE

We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC).

MATERIALS AND METHODS

A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated.

RESULTS

The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia.

CONCLUSION

Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.

Intratumoral and peritumoral MRI-based radiomics prediction of histopathological grade in soft tissue sarcomas: a two-center study

OBJECTIVES

This study aims to develop a model based on intratumoral and peritumoral radiomics from fat-suppressed T2-weighted(FS-T2WI) images to predict the histopathological grade of soft tissue sarcoma (STS).

METHODS

This retrospective study included 160 patients with STS from two centers, of which 82 were low-grade and 78were high-grade. Radiomics features were extracted and selected from the region of tumor mass volume (TMV) and peritumoral tumor volume (PTV) respectively. The TMV, PTV, and combined(TM-PTV) radiomics models were established in the training cohort (n = 111)for the prediction of histopathological grade. Finally, a radiomics nomogram was constructed by combining the TM-PTV radiomics signature (Rad-score) and the selected clinical-MRI predictor. The ROC and calibration curves were used to determine the performance of the TMV, PTV, and TM-PTV models in the training and validation cohort (n = 49). The decision curve analysis (DCA) and calibration curves were used to investigate the clinical usefulness and calibration of the nomogram, respectively.

RESULTS

The TMV model, PTV model, and TM-PTV model had AUCs of 0.835, 0.879, and 0.917 in the training cohort and 0.811, 0.756, 0.896 in the validation cohort. The nomogram, including the TM-PTV signatures and peritumoral hyperintensity, achieved good calibration and discrimination with a C-index of 0.948 (95% CI, 0.906 to 0.990) in the training cohort and 0.921 (95% CI, 0.840 to 0.995) in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the nomogram.

CONCLUSION

The proposed model based on intratumoral and peritumoral radiomics showed good performance in distinguishing low-grade from high-grade STSs.

Low-Dose CT Screening of Persistent Subsolid Lung Nodules: First-Order Features in Radiomics

BACKGROUND

 Nondisappearing subsolid nodules requiring follow-up are often detected during lung cancer screening, but changes in their invasiveness can be overlooked owing to slow growth. We aimed to develop a method for automatic identification of invasive tumors among subsolid nodules during multiple health checkups using radiomics technology based on low-dose computed tomography (LD-CT) and examine its effectiveness.

METHODS

 We examined patients who underwent LD-CT screening from 2014 to 2019 and had lung adenocarcinomas resected after 5-year follow-ups. They were categorized into the invasive or less-invasive group; the annual growth/change rate (Δ) of the nodule voxel histogram using three-dimensional CT (e.g., tumor volume, solid volume percentage, mean CT value, variance, kurtosis, skewness, and entropy) was assessed. A discriminant model was designed through multivariate regression analysis with internal validation to compare its efficacy with that of a volume doubling time of < 400 days.

RESULTS

 The study included 47 tumors (23 invasive, 24 less invasive), with no significant difference in the initial tumor volumes. Δskewness was identified as an independent predictor of invasiveness (adjusted odds ratio, 0.021; p = 0.043), and when combined with Δvariance, it yielded high accuracy in detecting invasive lesions (88% true-positive, 80% false-positive). The detection model indicated surgery 2 years earlier than the volume doubling time, maintaining accuracy (median 3 years vs.1 year before actual surgery, p = 0.011).

CONCLUSION

 LD-CT radiomics showed promising potential in ensuring timely detection and monitoring of subsolid nodules that warrant follow-up over time.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

The Association of Gross Tumor Volume and Its Radiomics Features with Brain Metastases Development in Patients with Radically Treated Stage III Non-Small Cell Lung Cancer

PURPOSE

To identify clinical risk factors, including gross tumor volume (GTV) and radiomics features, for developing brain metastases (BM) in patients with radically treated stage III non-small cell lung cancer (NSCLC).

METHODS

Clinical data and planning CT scans for thoracic radiotherapy were retrieved from patients with radically treated stage III NSCLC. Radiomics features were extracted from the GTV, primary lung tumor (GTVp), and involved lymph nodes (GTVn), separately. Competing risk analysis was used to develop models (clinical, radiomics, and combined model). LASSO regression was performed to select radiomics features and train models. Area under the receiver operating characteristic curves (AUC-ROC) and calibration were performed to assess the models' performance.

RESULTS

Three-hundred-ten patients were eligible and 52 (16.8%) developed BM. Three clinical variables (age, NSCLC subtype, and GTVn) and five radiomics features from each radiomics model were significantly associated with BM. Radiomic features measuring tumor heterogeneity were the most relevant. The AUCs and calibration curves of the models showed that the GTVn radiomics model had the best performance (AUC: 0.74; 95% CI: 0.71-0.86; sensitivity: 84%; specificity: 61%; positive predictive value [PPV]: 29%; negative predictive value [NPV]: 95%; accuracy: 65%).

CONCLUSION

Age, NSCLC subtype, and GTVn were significant risk factors for BM. GTVn radiomics features provided higher predictive value than GTVp and GTV for BM development. GTVp and GTVn should be separated in clinical and research practice.

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

BACKGROUND

Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM.

METHODS

A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM.

RESULTS

The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC.

CONCLUSIONS

 Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.

Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis

Myocardial amyloidosis (CA) differs from other etiological pathologies of left ventricular hypertrophy in that transthoracic echocardiography is challenging to assess the texture features based on human visual observation. There are few studies on myocardial texture based on echocardiography. Therefore, this paper proposes an adaptive machine learning method based on ultrasonic image texture features to identify CA. In this retrospective study, a total of 289 participants (50 cases of myocardial amyloidosis; Hypertrophic cardiomyopathy: 70 cases; Uremic cardiomyopathy: 92 cases; Hypertensive heart disease: 77 cases). We extracted the myocardial ultrasonic imaging features of these patients and screened the features, and four models of random forest (RF), support vector machine (SVM), logistic regression (LR) and gradient decision-making lifting tree (GBDT) were established to distinguish myocardial amyloidosis from other diseases. Finally, the diagnostic efficiency of the model was evaluated and compared with the traditional ultrasonic diagnostic methods. In the overall population, the four machine learning models we established could effectively distinguish CA from nonCA diseases, AUC (RF 0.77, SVM 0.81, LR 0.81, GBDT 0.71). The LR model had the best diagnostic efficiency with recall, F1-score, sensitivity and specificity of 0.21, 0.34, 0.21 and 1.0, respectively. Slightly better than the traditional ultrasonic diagnosis model. In further subgroup analysis, the myocardial amyloidosis group was compared one-by-one with the patients with hypertrophic cardiomyopathy, uremic cardiomyopathy, and hypertensive heart disease groups, and the same method was used for feature extraction and data modeling. The diagnostic efficiency of the model was further improved. Notably, in identifying of the CA group and HHD group, AUC values reached more than 0.92, accuracy reached more than 0.87, sensitivity equal to or greater than 0.81, specificity 0.91, and F1 score higher than 0.84. This novel method based on echocardiography combined with machine learning may have the potential to be used in the diagnosis of CA.

Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer

OBJECTIVES

To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC).

METHODS

In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV₃, GPTV₆, GPTV₉). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.

RESULTS

Compared with GTV, GPTV₃, and GPTV₆ radiomics models, GPTV₉ radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV₉-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV₉-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05).

CONCLUSIONS

CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery.

KEY POINTS

• Compared with GTV, GPTV₃, and GPTV₆ radiomics models, GPTV₉ radiomics model showed better prediction performance for LVI status in NSCLC. • The radiomics nomogram based on GPTV₉ radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model. • The radiomics nomogram had a wider scope of clinical application.

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing-in terms of voxel size resampling, discretization, and filtering-on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson's or Spearman's correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson's and Spearman's dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features' stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps.

Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer

Objective

High-risk prostate cancer (PCa) is often treated by prostate-only radiotherapy (PORT) owing to its favourable toxicity profile compared to whole-pelvic radiotherapy. Unfortunately, more than 50% patients still developed disease progression following PORT. Conventional clinical factors may be unable to identify at-risk subgroups in the era of precision medicine. In this study, we aimed to investigate the prognostic value of pre-treatment planning computed tomography (pCT)-based radiomic features and clinical attributes to predict 5-year progression-free survival (PFS) in high-risk PCa patients following PORT.

Materials and methods

A total of 176 biopsy-confirmed PCa patients who were treated at the Hong Kong Princess Margaret Hospital were retrospectively screened for eligibility. Clinical data and pCT of one hundred eligible high-risk PCa patients were analysed. Radiomic features were extracted from the gross-tumour-volume (GTV) with and without applying Laplacian-of-Gaussian (LoG) filter. The entire patient cohort was temporally stratified into a training and an independent validation cohort in a ratio of 3:1. Radiomics (R), clinical (C) and radiomic-clinical (RC) combined models were developed by Ridge regression through 5-fold cross-validation with 100 iterations on the training cohort. A model score was calculated for each model based on the included features. Model classification performance on 5-year PFS was evaluated in the independent validation cohort by average area-under-curve (AUC) of receiver-operating-characteristics (ROC) curve and precision-recall curve (PRC). Delong's test was used for model comparison.

Results

The RC combined model which contains 6 predictive features (tumour flatness, root-mean-square on fine LoG-filtered image, prostate-specific antigen serum concentration, Gleason score, Roach score and GTV volume) was the best-performing model (AUC = 0.797, 95%CI = 0.768-0.826), which significantly outperformed the R-model (AUC = 0.795, 95%CI = 0.774-0.816) and C-model (AUC = 0.625, 95%CI = 0.585-0.665) in the independent validation cohort. Besides, only the RC model score significantly classified patients in both cohorts into progression and progression-free groups regarding their 5-year PFS (p< 0.05).

Conclusion

Combining pCT-based radiomic and clinical attributes provided superior prognostication value regarding 5-year PFS in high-risk PCa patients following PORT. A large multi-centre study will potentially aid clinicians in implementing personalised treatment for this vulnerable subgroup in the future.

Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review

OBJECTIVES

To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies.

METHODS

The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall.

CONCLUSIONS

Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed.

KEY POINTS

• Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.

Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI

Purpose

This study aims to investigate values of intra- and peri-tumoral regions in the mammography and magnetic resonance imaging (MRI) image for prediction of sentinel lymph node metastasis (SLNM) in invasive breast cancer (BC).

Methods

This study included 208 patients with invasive BC between Spe. 2017 and Apr. 2021. All patients underwent preoperative digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) scans. Radiomics features were extracted from manually outlined intratumoral regions, and automatically dilated peritumoral tumor regions in each modality. The least absolute shrinkage and selection operator (LASSO) regression was used to select key features from each region to develop radiomics signatures (RSs). Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and negative predictive value (NPV) were calculated to evaluate performance of the RSs.

Results

Intra- and peri-tumoral regions of BC can provide complementary information on the SLN status. In each modality, the Com-RSs derived from combined intra- and peri-tumoral regions always yielded higher AUCs than the Intra-RSs or Peri-RSs. A total of 10 and 11 features were identified as the most important predictors from mammography (DM plus DBT) and MRI (DCE-MRI plus DWI), respectively. The DCE-MRI plus DWI generated higher AUCs compared with DM plus DBT in the training (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.897 vs. 0.846) and validation (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.826 vs. 0.786) cohort.

Conclusions

Radiomics features from intra- and peri-tumoral regions can provide complementary information to identify the SLNM in both mammography and MRI. The DCE-MRI plus DWI generated lower specificity, but higher AUC, accuracy, sensitivity and negative predictive value compared with DM plus DBT.

Role of intratumoral and peritumoral CT radiomics for the prediction of EGFR gene mutation in primary lung cancer

Objectives

To determine the added value of combining intratumoral and peritumoral CT radiomics for the prediction of epidermal growth factor receptor (EGFR) gene mutations in primary lung cancer (PLC).

Methods

This study included 478 patients with PLC (348 adenocarcinomas and 130 other histological types) who underwent surgical resection and EGFR gene testing. Two radiologists performed segmentation of tumors and peritumoral regions using precontrast high-resolution CT images, and 398 radiomic features (212 intra- and 186 peritumoral features) were extracted. The peritumoral region was defined as the lung parenchyma within a distance of 3 mm from the tumor border. Model performance was estimated using Random Forest, a machine-learning algorithm.

Results

EGFR mutations were found in 162 tumors; 161 adenocarcinomas, and one pleomorphic carcinoma. After exclusion of poorly reproducible and redundant features, 32 radiomic features remained (14 intra- and 18 peritumoral features) and were included in the model building. For predicting EGFR mutations, combining intra- and peritumoral radiomics significantly improved the performance compared to intratumoral radiomics alone (AUC [area under the receiver operating characteristic curve], 0.774 vs 0.730; p < 0.001). Even in adenocarcinomas only, adding peritumoral radiomics significantly increased performance (AUC, 0.687 vs 0.630; p < 0.001). The predictive performance using radiomics and clinical features was significantly higher than that of clinical features alone (AUC, 0.826 vs 0.777; p = 0.005).

Conclusions

Combining intra- and peritumoral radiomics improves the predictive accuracy of EGFR mutations and could be used to aid in decision-making of whether to perform biopsy for gene tests.

Advances in knowledge

Adding peritumoral to intratumoral radiomics yields greater accuracy than intratumoral radiomics alone in predicting EGFR mutations and may serve as a non-invasive method of predicting of the gene status in PLC.

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study

The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.

Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer

PURPOSE This retrospective study aims to evaluate the use of multi-parametric magnetic resonance imaging (MRI) in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer using radiomics methods. METHODS A total of 163 patients who underwent contrast-enhanced T1-weighted (CE T1W) and T2-weighted (T2W) MRI scans at 3.0T were enrolled between January 2014 and September 2019. Radiomics features were extracted and selected from the tumoral and peritumoral regions at different dilation distances outside the tumor. Mann-Whitney U test, the least absolute shrinkage and selection operator logistic regression, and logistic regression was applied to select the predictive features and develop the radiomics signature. Univariate analysis was performed on the clinical characteristics. The radiomics nomogram was constructed incorporating the radiomics signature and the selected important clinical predictor. Prediction performance of the radiomics signature, clinical model, and nomogram was evaluated with the area under the curve (AUC), specificity, sensitivity, calibration, and decision curve analysis (DCA). RESULTS A total of 5 features that were selected from the peritumoral regions with 3- and 7-mm dilation distances outside tumors in CE T1W and T2W MRI, respectively, showed optimal discriminative performance. The radiomics signature comprising the selected features was significantly associated with the LVSI status. The radiomics nomogram integrating the radiomics signature and degree of cellular differentiation exhibited the best predictability with AUCs of 0.771 (specificity (SPE)=0.831 and sensitivity (SEN)=0.581) in the training cohort and 0.788 (SPE=0.727, SEN=0.773) in the validation cohort. DCA confirmed the clinical usefulness of our model. CONCLUSION Our results illustrate that the radiomics nomogram based on MRI features from peritumoral regions and the degree of cellular differentiation can be used as a noninvasive tool for predicting LVSI in cervical cancer.

MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery

PURPOSE

This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS).

METHODS

The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS. Analysis was performed on original 3D MR images and after their laplacian of gaussian (LoG), logarithm and exponential filtering. The prediction performance was evaluated by Pearson correlation, linear regression and ROC analysis, with meningioma volume change per month as the outcome.

RESULTS

Sixty calculated features significantly correlated with the outcome. The feature selection based on LASSO and multivariate regression started from all available 337 radiomic and 12 non-radiomic features. It selected LoG-sigma-1-0-mm-3D_firstorder_InterquartileRange and logarithm_ngtdm_Busyness as the predictively most robust and non-redundant features. The radiomic score based on these two features produced an AUC = 0.81. Adding the non-radiomic karnofsky performance status (KPS) to the score has increased the AUC to 0.88. Low values of the radiomic score defined a homogeneous subgroup of 50 patients with consistent absence (0%) of tumor progression.

CONCLUSION

This is the first report of a strong association between MRI radiomic features and volumetric meningioma response to radiosurgery. The clinical importance of the early and reliable prediction of meningioma responsiveness to radiosurgery is based on its potential to aid individualized therapy decision making.

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.

Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study

Background

Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity.

Methods

CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed.

Results

All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81–0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, “kurtosis” had a high predictive value of early death (AUC at first year: 0.70–0.75 in two independent cohorts), negative association with histopathological grade (Spearman’s r: − 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915–11.561) than histopathological staging and grading.

Conclusions

We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.

Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers

BACKGROUND: Image-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes. METHODS: Data and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes. RESULTS: Decision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS). CONCLUSION: We utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.

Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography

In this study, we investigated the possibility of predicting expression levels of programmed death-ligand 1 (PD-L1) using radiomic features of intratumoral and peritumoral tumors on computed tomography (CT) images. We retrospectively analyzed 161 patients with non-small cell lung cancer. We extracted radiomics features for intratumoral and peritumoral regions on CT images. The null importance, least absolute shrinkage, and selection operator model were used to select the optimized feature subset to build the prediction models for the PD-L1 expression level. LightGBM with five-fold cross-validation was used to construct the prediction model and evaluate the receiver operating characteristics. The corresponding area under the curve (AUC) was calculated for the training and testing cohorts. The proportion of ambiguously clustered pairs was calculated based on consensus clustering to evaluate the validity of the selected features. In addition, Radscore was calculated for the training and test cohorts. For expression level of PD-L1 above 1%, prediction models that included radiomic features from the intratumoral region and a combination of radiomic features from intratumoral and peritumoral regions yielded an AUC of 0.83 and 0.87 and 0.64 and 0.74 in the training and test cohorts, respectively. In contrast, the models above 50% prediction yielded an AUC of 0.80, 0.97, and 0.74, 0.83, respectively. The selected features were divided into two subgroups based on PD-L1 expression levels ≥ 50% or ≥ 1%. Radscore was statistically higher for subgroup one than subgroup two when radiomic features for intratumoral and peritumoral regions were combined. We constructed a predictive model for PD-L1 expression level using CT images. The model using a combination of intratumoral and peritumoral radiomic features had a higher accuracy than the model with only intratumoral radiomic features.

Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases

Purpose

Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the ablative treatment of thoracic tumors. This study aims to identify predictors of treatment responses in patients affected by early stage non-small cell lung cancer (NSCLC) or pulmonary oligo-metastases treated with SBRT and to develop an accurate machine learning model to predict radiological response to SBRT.

Materials and Methods

Computed tomography (CT) images of 85 tumors (stage I–II NSCLC and pulmonary oligo-metastases) from 69 patients treated with SBRT were analyzed. Gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) or partial response (PR) were defined as responders. One hundred ten radiomic features were extracted using PyRadiomics module based on the GTV. The association of features with response to SBRT was evaluated. A model using support vector machine (SVM) was then trained to predict response based solely on the extracted radiomics features. Receiver operating characteristic curves were constructed to evaluate model performance of the identified radiomic predictors.

Results

Sixty-nine patients receiving thoracic SBRT from 2008 to 2018 were retrospectively enrolled. Skewness and root mean squared were identified as radiomic predictors of response to SBRT. The SVM machine learning model developed had an accuracy of 74.8%. The area under curves for CR, PR, and non-responder prediction were 0.86 (95% confidence interval [CI], 0.794–0.921), 0.946 (95% CI, 0.873–0.978), and 0.857 (95% CI, 0.789–0.915), respectively.

Conclusion

Radiomic analysis of pre-treatment CT scan is a promising tool that can predict tumor response to SBRT.

MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

PURPOSE

To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses.

METHODS

We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters.

RESULTS

Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models.

CONCLUSION

The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.

Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

Simple Summary

Radiation-induced hypothyroidism (RIHT) commonly develops in cancer survivors that receive radiation therapy for cancers in the head and neck region. The state-of-art normal tissue complication probability (NTCP) models perform satisfactorily; however, they do not use the whole spectrum of information that can be obtained from imaging techniques. The radiomic approach offers the ability to efficiently mine features, which are imperceptible to the human eye, but may provide crucial data about the patient’s condition. We gathered CT images and clinical data from 98 patients undergoing radiotherapy for head and neck cancers, 27 of whom later developed RIHT. For them, we created machine-learning models to predict RIHT using automatically extracted radiomic features and appropriate clinical and dosimetric parameters. We also validated the well-established external state-of-art NTCP models on our datasets and observed that our radiomic-based models performed very similarly to them. This shows that automated tools may perform as well as the current standard but can be theoretically applied faster and be implemented into existing imaging software used when planning radiotherapy.

Abstract

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.

Viral replication in human macrophages enhances an inflammatory cascade and interferon driven chronic COVID-19 in humanized mice

Chronic COVID-19 is characterized by persistent viral RNA and sustained interferon (IFN) response which is recapitulated and required for pathology in SARS-CoV-2 infected MISTRG6-hACE2 humanized mice. As in the human disease, monocytes, and macrophages in SARS-CoV-2 infected MISTRG6-hACE2 are central to disease pathology. Here, we describe SARS-CoV-2 uptake in tissue resident human macrophages that is enhanced by virus specific antibodies. SARS-CoV-2 replicates in these human macrophages as evidenced by detection of double-stranded RNA, subgenomic viral RNA and expression of a virally encoded fluorescent reporter gene; and it is inhibited by Remdesivir, an inhibitor of viral replication. Although early IFN deficiency leads to enhanced disease, blocking either viral replication with Remdesivir or the downstream IFN stimulated cascade by injecting anti-IFNAR2 in vivo in the chronic stages of disease attenuates many aspects of the overactive immune-inflammatory response, especially the inflammatory macrophage response, and most consequentially, the chronic disease itself.

Radiomics for predicting perineural invasion status in rectal cancer

BACKGROUND

Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging.

AIM

To establish a radiomics model for evaluating PNI status preoperatively in RC patients.

METHODS

This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).

RESULTS

One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort).

CONCLUSION

The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Machine learning model development for quantitative analysis of CT heterogeneity in canine hepatic masses may predict histologic malignancy

Tumor heterogeneity is a well-established marker of biologically aggressive neoplastic processes and is associated with local recurrence and distant metastasis. Quantitative analysis of CT textural features is an indirect measure of tumor heterogeneity and therefore may help predict malignant disease. The purpose of this retrospective, secondary analysis study was to quantitatively evaluate CT heterogeneity in dogs with histologically confirmed liver masses to build a predictive model for malignancy. Forty dogs with liver tumors and corresponding histopathologic evaluation from a previous prospective study were included. Triphasic image acquisition was standardized across dogs and whole liver and liver mass were contoured on each precontrast and delayed postcontrast dataset. First-order and second-order indices were extracted from contoured regions. Univariate analysis identified potentially significant indices that were subsequently used for top-down model construction. Multiple quadratic discriminatory models were constructed and tested, including individual models using both postcontrast and precontrast whole liver or liver mass volumes. The best performing model utilized the CT features voxel volume and uniformity from postcontrast mass contours; this model had an accuracy of 0.90, sensitivity of 0.67, specificity of 1.0, positive predictive value of 1.0, negative predictive value of 0.88, and precision of 1.0. Heterogeneity indices extracted from delayed postcontrast CT hepatic mass contours were more informative about tumor type compared to indices from whole liver contours, or from precontrast hepatic mass and whole liver contours. Results demonstrate that CT radiomic feature analysis may hold clinical utility as a noninvasive method of predicting hepatic malignancy and may influence diagnostic or therapeutic approaches.

[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]

Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.

AWGS2019 vs EWGSOP2 for diagnosing sarcopenia to predict long-term prognosis in Chinese patients with gastric cancer after radical gastrectomy

BACKGROUND

Sarcopenia is a nutrition-related disease and has a profound effect on the long-term overall survival (OS) of patients with gastric cancer. Its diagnostic criterion is critical to clinical diagnosis and treatment. However, previous research reported widely differing sarcopenia prevalence due to different criteria. AWGS2019 and EWGSOP2 are the two latest and widely adopted criteria.

AIM

To compare the effects of AWGS2019 and EWGSOP2 on the long-term OS of Chinese gastric cancer patient after radical gastrectomy.

METHODS

An observational study was conducted from July 2014 to January 2017, which included 648 consecutive gastric cancer patients who underwent radical gastrectomy. The sarcopenia elements (skeletal muscle index, handgrip strength, and gait speed) were measured within 1 mo or 7 d before surgery. The patients were followed at fixed intervals to gain the outcomes. Multivariate Cox regression analysis was performed to determine the association between sarcopenia and the long-term OS of these patients according to the two criteria separately. The predictive performance of the models with AWGS2019 and EWGSOP2 were evaluated by the concordance index (C-index) and area under the time-dependent receiver operating characteristic curve (AUC). The Akaike information criterion (AIC) was applied to compare model fits.

RESULTS

The prevalence of sarcopenia was 20.5% and 11.3% according to AWGS2019 and EWGSOP2, respectively. Sarcopenia was an independent risk factor for the long-term OS no matter based on AWGS2019 or EWGSOP2, but AWGS2019-sarcopenia in multivariate model had a higher hazard ratio (HR) [2.150 (1.547-2.988)] than EWGSOP2-sarcopenia [HR 1.599 (1.092-2.339)]. Meanwhile, the model with AWGS2019-sarcopenia [C-index 0.773 (0.742-0.804); AIC 2193.7; time-dependent AUC 0.812 (0.756-0.867) for 1-year OS, 0.815 (0.778-0.852) for 3-year OS, and 0.809 (0.759-0.859) for 5-year OS] had better predictive power and model fits than the model with EWGSOP2-sarcopenia [C-index 0.762 (0.729-0.795); AIC 2215.2; time-dependent AUC 0.797 (0.741-0.854) for 1-year OS, 0.804 (0.767-0.842) for 3-year OS, and 0.799 (0.748-0.850) for 5-year OS].

CONCLUSION

Sarcopenia is an independent risk factor for the long-term OS in Chinese gastric cancer patients undergoing radical gastrectomy. The prediction model with AWGS2019-sarcopenia has better predictive power and model fits than the prediction model with EWGSOP2-sarcopenia. AWGS2019 may be more appropriate for diagnosing sarcopenia in these Chinese patients than EWGSOP2.

Cancer heterogeneity and metastasis: life at the edge

There is abundant evidence that the phenotype of cells the tumor at the stromal interface is distinct from the tumor cells that are within the core. Molecular phenotyping of cells at the edge show that they express higher levels of proteins associated with elevated glycolytic metabolism, including GLUT-1, HIF-1, and CA-IX. An end product of glycolysis is the production of acid, and acidosis of tumors is strongly associated with increased metastatic potential across a wide variety of tumor types. The molecular machinery promoting this export of acid is being defined, with close collaboration between carbonic anhydrases, sodium dependent bicarbonate and monocarboxylate transporters. Neutralization of this acidity can prevent local invasion and metastasis, and this has led to the "acid-mediated invasion hypothesis" wherein export of acid from the tumor into the stroma leads to matrix remodeling, which can promote local invasion.