Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer

Computed-tomography-based radiomic nomogram for predicting the risk of indeterminate small (5-20 mm) solid pulmonary nodules

PURPOSE

This study aims to develop a diagnostic model that combines computed tomography (CT) images and radiomic features to differentiate indeterminate small (5-20 mm) solid pulmonary nodules (SSPNs).

METHODS

This study retrospectively enrolled 413 patients who had had SSPNs surgically removed and histologically confirmed between 2017 and 2019. The SSPNs included solid malignant pulmonary nodules (n = 210) and benign pulmonary nodules (n = 203). The least absolute shrinkage and selection operator was used for radiomic feature selection, and random forest algorithms were used for radiomic model construction. The clinical model and nomogram were established using univariate and multivariable logistic regression analyses combined with clinical symptoms, subjective CT findings, and radiomic features. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the performance of the models.

RESULTS

The AUC for the clinical model was 0.77 in the training cohort [n = 289; 95% confidence interval (CI): 0.71-0.82; P = 0.001] and 0.75 in the validation cohort (n = 124; 95% CI: 0.66-0.83; P = 0.016). The AUCs for the nomogram were 0.92 (95% CI: 0.89-0.95; P < 0.001) and 0.85 (95% CI: 0.78-0.91; P < 0.001), respectively. The radiomic score (Rad-score), sex, pleural indentation, and age were the independent predictors that were used to build the nomogram.

CONCLUSION

The radiomic nomogram derived from clinical features, subjective CT signs, and the Rad-score can potentially identify the risk of indeterminate SSPNs and aid in the patient's preoperative diagnosis.

Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study

The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.

Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians

Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.

Structural and functional radiomics for lung cancer

INTRODUCTION

Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.

METHODS

Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.

CONCLUSION

The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Pleural staging using local anesthetic thoracoscopy in dry pleural dissemination and minimal pleural effusion

BACKGROUND

Dry pleural dissemination (DPD) and minimal (<10 mm thick) pleural effusion (PE) may be discovered intraoperatively as unexpected metastases. A definitive diagnostic procedure such as pleural biopsy is rarely attempted in such patients preoperatively. We retrospectively investigated the use and safety of local anesthetic thoracoscopy (LAT) as a pleural staging tool in the diagnosis of DPD and minimal PE.

METHODS

We reviewed 18 patients with non-small cell lung cancer (radiological DPD and minimal PE in 13 and five patients, respectively) who underwent LAT using a flex-rigid pleuroscope for pleural staging from April 2015 to September 2020.

RESULTS

The median age of the patients was 72 years. Nine patients (50%) were men. The dominant histological type was adenocarcinoma (n = 16). Three patients each with radiological DPD and minimal PE had visible PE on the LAT. Pleural biopsy was performed in the 16 cases in which pleural abnormalities were identified. On pleural staging, five cases were diagnosed without pleural dissemination (M0), and 13 cases were diagnosed with pleural dissemination (M1a). Only one case in which the lesion could not be identified because of pleural adhesions was false-negative. The success rates for pleural staging, sensitivity, specificity, positive predictive value, and negative predictive value were 94.4% (17/18), 92.8% (13/14), 100% (4/4), 100% (13/13), and 80.0% (4/5), respectively. There were no lung lacerations or other severe complications caused by the procedure or during blunt dissection.

CONCLUSION

LAT might be a useful tool for accurate pleural staging in cases with DPD and minimal PE suspected radiologically.

A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer

Only 20% NSCLC patients benefit from immunotherapy with a durable response. Current biomarkers are limited by the availability of samples and do not accurately predict who will benefit from immunotherapy. To develop a unified deep learning model to integrate multimodal serial information from CT with laboratory and baseline clinical information. We retrospectively analyzed 1633 CT scans and 3414 blood samples from 200 advanced stage NSCLC patients who received single anti-PD-1/PD-L1 agent between April 2016 and December 2019. Multidimensional information, including serial radiomics, laboratory data and baseline clinical data, was used to develop and validate deep learning models to identify immunotherapy responders and nonresponders. A Simple Temporal Attention (SimTA) module was developed to process asynchronous time-series imaging and laboratory data. Using cross-validation, the 90-day deep learning-based predicting model showed a good performance in distinguishing responders from nonresponders, with an area under the curve (AUC) of 0.80 (95% CI: 0.74-0.86). Before immunotherapy, we stratified the patients into high- and low-risk nonresponders using the model. The low-risk group had significantly longer progression-free survival (PFS) (8.4 months, 95% CI: 5.49-11.31 vs. 1.5 months, 95% CI: 1.29-1.71; HR 3.14, 95% CI: 2.27-4.33; log-rank test, P<0.01) and overall survival (OS) (26.7 months, 95% CI: 18.76-34.64 vs. 8.6 months, 95% CI: 4.55-12.65; HR 2.46, 95% CI: 1.73-3.51; log-rank test, P<0.01) than the high-risk group. An exploratory analysis of 93 patients with stable disease (SD) [after first efficacy assessment according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1] also showed that the 90-day model had a good prediction of survival and low-risk patients had significantly longer PFS (11.1 months, 95% CI: 10.24-11.96 vs. 3.3 months, 95% CI: 0.34-6.26; HR 2.93, 95% CI: 1.69-5.10; log-rank test, P<0.01) and OS (31.7 months, 95% CI: 23.64-39.76 vs. 17.2 months, 95% CI: 7.22-27.18; HR 2.22, 95% CI: 1.17-4.20; log-rank test, P=0.01) than high-risk patients. In conclusion, the SimTA-based multi-omics serial deep learning provides a promising methodology for predicting response of advanced NSCLC patients to anti-PD-1/PD-L1 monotherapy. Moreover, our model could better differentiate survival benefit among SD patients than the traditional RECIST evaluation method.

PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines

This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non-small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive "NSCLC Radiomics" data collection. Four hundred and two thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy-eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert-vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y-gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs - where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from "NSCLC Radiomics," pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.

Predictive factors related to pleural dissemination in non-small cell lung cancer

Background

The prognosis of non-small-cell lung cancer (NSCLC) patients with pleural dissemination is poor, and pleural dissemination is generally considered a contraindication for radical surgery. However, if pleural dissemination is missed intraoperatively, patients with false-negative stage IV NSCLC cannot receive appropriate chemotherapy, and their prognosis might worsen.

Methods

In the present study, we enrolled 144 patients who received surgery for NSCLC between January 2008 and December 2019 with available data on the maximum standardized uptake value (SUV_max) on positron emission tomography (PET) with lesions adjacent to the visceral pleura and without lesions invading the chest wall.

Results

Seven patients who had pleural dissemination were compared with 137 patients who had not pleural dissemination. The relationships between pleural dissemination and the clinicopathological variables were analyzed, and significant differences in the histopathological type (P=0.03), and differentiation (P<0.01) were noted. It was suggested that squamous cell carcinoma tended not to show dissemination to the pleural cavity. The logistic regression analyses of the predictive factors related to pleural dissemination in non-squamous cell carcinoma patients were analyzed, and the age (P=0.01) and differentiation (P<0.01) were identified as significant predictive factors related to pleural dissemination.

Conclusions

Cases with non-squamous cell carcinoma, a young age, and poor differentiation of undifferentiated grade of histological differentiation are factors associated with early pleural cavity dissemination.

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules

Background

To test the ability of a multiclassifier model based on radiomics features to predict benign and malignant primary pulmonary solid nodules.

Methods

Computed tomography (CT) images of 342 patients with primary pulmonary solid nodules confirmed by histopathology or follow-up were retrospectively analyzed. The region of interest (ROI) of the images was delineated, and the radiomics features of the lesions were extracted. The feature weight was calculated using the relief feature selection algorithm. Based on the selected features, five classifier models were constructed: support vector machine (SVM), random forest (RF), logistic regression (LR), extreme learning machine (ELM), and K-nearest neighbor (KNN). The precision, recall rate, and area under the receiver operating characteristic curve (AUC) were used to evaluate the prediction performance of each classifier. The prediction result of each classifier was first weighted, and then all the prediction results were fused to predict the nodule type of unknown images. The prediction precision, recall rate, and AUC of the fusion classifier and single classifier were compared. Cross-validation was used to evaluate the generalization of the fusion classifier, and t- and F-tests were performed on the five classifiers and fusion classifier.

Results

For each ROI, 450 features in four major categories were extracted and were analyzed using the relief feature selection algorithm. According to the weights, 25 highly repetitive and nonredundant stable features that played a major role in pulmonary nodule classification were selected. The fusion classifier’s prediction performance (prediction precision =92.0%, AUC =0.915) was superior to those of SVM (prediction precision =75.3%, AUC =0.740), RF (prediction precision =89.1%, AUC =0.855), LR (prediction precision =68.4%, AUC =0.681), ELM (prediction precision =87.0%, AUC =0.830), and KNN (prediction precision =77.1%, AUC =0.702). The fusion classifier showed the best null hypothesis performance in the t-test (P=0.035) and F-test (P=0.036).

Conclusions

The multiclassifier fusion model based on radiomics features had high prediction value for benign and malignant primary pulmonary solid nodules.