Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification

Generative Adversarial Networks With Radiomics Supervision for Lung Lesion Generation

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.

Lung nodule classification using radiomics model trained on degraded SDCT images

BACKGROUND AND OBJECTIVE

Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challenges, we propose a novel approach using synthetic LDCT images generated from standard-dose CT (SDCT) scans from the LIDC-IDRI dataset. Our objective is to develop and validate an interpretable radiomics-based model for distinguishing likely benign from likely malignant pulmonary nodules.

METHODS

From a total of 1010 CT images (695 SDCTs and 315 LDCTs), we degraded SDCTs in the sinogram domain and obtained 1950 nodules as the training set. The 675 nodules from the LDCTs were stratified into 50%-50% partitions for validation and testing. Radiomic features were extracted from nodules, and three feature sets were assessed using: a) only shape and size (SS) features, b) all features but SS features, and c) all features. A systematic pipeline was developed to optimize the feature set and evaluate multiple machine learning models. Models were trained using degraded SDCT, validated and tested on the LDCT nodules.

RESULTS

Training a logistic regression model using three SS features yielded the most promising results, achieving on the test set mean balanced accuracy, sensitivity, specificity, and AUC-ROC scores of 0.81, 0.76, 0.85, and 0.87, respectively.

CONCLUSIONS

Our study demonstrates the feasibility and effectiveness of using synthetic LDCT images for developing a relatively accurate radiomics-based model in lung nodule classification. This approach addresses challenges associated with LDCT screening, offering potential implications for improving lung cancer detection and reducing false positives.

An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT-Derived Nodules and Adipose Tissue: A Multicenter Study

BACKGROUND

Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules.

METHODS

One thousand and ninety-eight patients with 6-30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I-T), and two external test cohorts (E-T1, E-T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account.

RESULTS

The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p < 0.05, IDI = 0.137, p < 0.05). In the I-T, E-T1, and E-T2, the AUCs of DLRCN were 0.946 (95% CI: 0.936, 0.955), 0.948 (95% CI: 0.933, 0.963) and 0.962 (95% CI: 0.945, 0.979), The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (p > 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules.

CONCLUSION

The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN.

Facing an un-met need in lung cancer screening: The never smokers

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide and the second most common cancer in both men and women. In addition to smoking, other risk factors, such as environmental tobacco smoke, air pollution, biomass combustion, radon gas, occupational exposure, lung disease, family history of cancer, geographic variability, and genetic factors, play an essential role in developing LC. Current screening guidelines and eligibility criteria have limited efficacy in identifying LC cases (50 %), as most screening programs primarily target subjects with a smoking history as the leading risk factor. Implementing LC screening programs in people who have never smoked (PNS) can significantly impact cancer-specific survival and early disease detection. However, the available evidence regarding the feasibility and effectiveness of such programs is limited. Therefore, further research on LC screening in PNS is warranted to determine the necessary techniques for accurately identifying individuals who should be included in screening programs.

Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model

INTRODUCTION

Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN).

METHODOLOGY

Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes' theorem.

RESULTS

The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80.

CONCLUSIONS

Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

Imaging of Solid Pulmonary Nodules

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA

While CT lung cancer screening reduces lung cancer-specific mortality, there are remaining challenges. Radiomic tools promiss to address these challenges, however, they are subject to interobserver variability if semi-automated segmentation techniques are used. Herein we report interobserver variability for two validated radiomic tools, BRODERS (Benign versus aggRessive nODule Evaluation using Radiomic Stratification) and CANARY (Computer-Aided Nodule Assessment and Risk Yield). We retrospectively analyzed the CT images of 95 malignant lung nodules of the adenocarcinoma spectrum using BRODERS and CANARY. Cases were identified at Mayo Clinic (n = 45) and Vanderbilt University Medical Center and Nashville/Veteran Administration Tennessee Valley Health Care System (n = 50). Three observers with different training levels (medical student, internal medicine resident and thoracic radiology fellow) each performed lung nodule segmentation. All methods were carried out in accordance with relevant guidelines and regulations. Interclass correlation coefficients (ICC) of 0.77, 0.98 and 0.97 for the average nodule volume, BRODERS cancer probability and Score Indicative of Lesion Aggression (SILA) which summarizes the distribution of the CANARY exemplars indicated good to excellent reliability, respectively. The dice similarity coefficient was 0.79 and 0.81 for the data sets from the two institutions. BRODERS and CANARY are robust radiomics tools with excellent interobserver variability. These tools are simple and reliable regardless the observer/operator's level of training.

Pulmonary sclerosing pneumocytoma - approaching a solitary pulmonary nodule and the limitations of risk prediction models

Our case is an asymptomatic, non-smoking, East Asian woman in her 40s presenting with a solitary pulmonary nodule (SPN). On imaging, the 1.7 cm solid SPN located in the left upper lobe, was rounded in morphology and moderately fluorodeoxyglucose avid. The clinical pretest probability of malignancy assessed by risk prediction models such as Brock (19.1%), Mayo Clinic (56.2%) and Herder (51.4%) was discordant. She underwent a percutaneous CT-guided needle biopsy, establishing a diagnosis of pulmonary sclerosing pneumocytoma (PSP). PSP is a rare benign lung neoplasm with indolent growth characteristics that has been described predominantly in non-smoking women. Our case illustrates the limitations of applying existing risk prediction models in Asia where the epidemiology and biology of lung cancer differ significantly from the Caucasian derivation cohorts. Additionally, the risk models do not account for tuberculosis, which is endemic in Asia and can mimic malignancy. Non-surgical lung biopsy remains useful in minimising unnecessary thoracotomy.

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models

The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.

A Series-Based Deep Learning Approach to Lung Nodule Image Classification

Although many studies have shown that deep learning approaches yield better results than traditional methods based on manual features, CADs methods still have several limitations. These are due to the diversity in imaging modalities and clinical pathologies. This diversity creates difficulties because of variation and similarities between classes. In this context, the new approach from our study is a hybrid method that performs classifications using both medical image analysis and radial scanning series features. Hence, the areas of interest obtained from images are subjected to a radial scan, with their centers as poles, in order to obtain series. A U-shape convolutional neural network model is then used for the 4D data classification problem. We therefore present a novel approach to the classification of 4D data obtained from lung nodule images. With radial scanning, the eigenvalue of nodule images is captured, and a powerful classification is performed. According to our results, an accuracy of 92.84% was obtained and much more efficient classification scores resulted as compared to recent classifiers.

Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign

OBJECTIVES

To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs).

MATERIALS AND METHODS

A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application.

RESULTS

The 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC_65keV-AP = 0.92, AUC_65keV-VP = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability.

CONCLUSIONS

Among the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Z_eff-AP was significantly superior to conventional model in the discrimination of SPSNs.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

Clinical Radiobiology for Radiation Oncology

This chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.

Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach

The decision to engage in lung cancer screening (LCS) necessitates weighing benefits versus harms. Previously, clinicians in the United States have used the PLCOM2012 algorithm to guide LCS decision-making. However, that formula contains race and gender-based variables. Previously, using data from a European study, Bojesen and colleagues have suggested that cg05575921 methylation could guide decision-making. To test this hypothesis in a more diverse American population, we examined DNA and clinical data from 3081 subjects from the National Lung Screening Trial (NLST) study. Using survival analysis, we found a simple linear predictor consisting of age, pack-year consumption and cg05575921, to have the best predictive power among several alternatives (AUC = 0.66). Results showed that the highest quartile of risk was more than 2-fold more likely to develop lung cancer than those in the lowest quartile. Race, ethnicity, and gender had no effect on prediction with both cg05575921 and pack years contributing equally (both p < 0.003) to risk prediction. Current smokers had considerably lower methylation than former smokers (46% vs 67%; p < 0.001) with the average methylation of those who quit approaching 80% after 25 years of cessation. Finally, current male smokers had lower mean cg05575921 percentage than female smokers (46% vs 49%; p < 0.001). We conclude that cg05575921 (along with age and pack years) can be used to guide LCS decision-making, and additional studies might focus on how best to use methylation to inform decision-making.

Non-Invasive Biomarkers for Early Lung Cancer Detection

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.

Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review

Lung cancer screening by low-dose computed tomography (LDCT) can save lives. Nevertheless, the test suffers from low accuracy. Improving its accuracy will reduce unnecessary invasive procedures and allow lung cancer treatment to be delivered sooner. This review describes the principles, advantages, and disadvantages of selected emerging modalities potentially useful to improve the accuracy of LDCT. A literature search was conducted using PubMed and Google scholar for relevant publications. We identified four key emerging approaches: radiomics, breath analysis, urine test, and blood test. Radiomics, which uses a computer program to extract various radiological features from radiographic images, holds the potential to improve the accuracy of LDCT. However, to date, there remains no adequately validated system. Breath analysis and urine tests represent a noninvasive and convenient means of screening by detecting substances such as volatile organic compounds associated with lung cancer. However, the results can be confounded by diets, medications, and concurrent medical conditions. Finally, a blood test to screen for protein biomarkers or methylation profiles such as Galleri® has high specificity. However, its sensitivity is low, especially for detecting early-stage lung cancer. Furthermore, the cost for mass public use can be significant. Based on our review, blood tests may have potential for future clinical utility. Its high specificity may be useful to rule in a suspicious lung nodule as malignant, so that other additional tests can be omitted. Data from a well-designed clinical trial will be needed to understand the clinical utility of this strategy.

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.

Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics

Purpose

In this study, we aimed to develop and validate a noninvasive method based on radiomics to evaluate the expression of Ki67 and prognosis of patients with non-small-cell lung cancer (NSCLC). Patients and Methods. A total of 120 patients with NSCLC were enrolled in this retrospective study. All patients were randomly assigned to a training dataset (n = 85) and test dataset (n = 35). According to the preprocessed F-FDG PET/CT image of each patient, a total of 384 radiomics features were extracted from the segmentation of regions of interest (ROIs). The Spearman correlation test and least absolute shrinkage and selection operator (LASSO), after normalization on the features matrix, were applied to reduce the dimensionality of the features. Furthermore, multivariable logistic regression analysis was used to propose a model for predicting Ki67. The survival curve was used to explore the prognostic significance of radiomics features.

Results

A total of 62 Ki67 positive patients and 58 Ki67 negative patients formed the training set and test training dataset and test dataset. Radiomics signatures showed good performance in predicting the expression of Ki67 with AUCs of 0.86 (training dataset) and 0.85 (test dataset). Validation and calibration showed that the radiomics had a strong predictive power in patients with NSCLC survival, which was significantly close to the effect of Ki67 expression on the survival of patients with NSCLC.

Conclusion

Radiomics signatures based on preoperative F-FDG PET/CT could distinguish the expression of Ki67, which also had a strong predictive performance for the survival outcome.

Developing an understanding of artificial intelligence lung nodule risk prediction using insights from the Brock model

Objectives

To determine if predictions of the Lung Cancer Prediction convolutional neural network (LCP-CNN) artificial intelligence (AI) model are analogous to the Brock model.

Methods

In total, 10,485 lung nodules in 4660 participants from the National Lung Screening Trial (NLST) were analysed. Both manual and automated nodule measurements were inputted into the Brock model, and this was compared to LCP-CNN. The performance of an experimental AI model was tested after ablating imaging features in a manner analogous to removing predictors from the Brock model. First, the nodule was ablated leaving lung parenchyma only. Second, a sphere of the same size as the nodule was implanted in the parenchyma. Third, internal texture of both nodule and parenchyma was ablated.

Results

Automated axial diameter (AUC 0.883) and automated equivalent spherical diameter (AUC 0.896) significantly improved the accuracy of Brock when compared to manual measurement (AUC 0.873), although not to the level of the LCP-CNN (AUC 0.936). Ablating nodule and parenchyma texture (AUC 0.915) led to a small drop in AI predictive accuracy, as did implanting a sphere of the same size as the nodule (AUC 0.889). Ablating the nodule leaving parenchyma only led to a large drop in AI performance (AUC 0.717).

Conclusions

Feature ablation is a feasible technique for understanding AI model predictions. Nodule size and morphology play the largest role in AI prediction, with nodule internal texture and background parenchyma playing a limited role. This is broadly analogous to the relative importance of morphological factors over clinical factors within the Brock model.

Key Points

• Brock lung cancer risk prediction accuracy was significantly improved using automated axial or equivalent spherical measurements of lung nodule diameter, when compared to manual measurements.

• Predictive accuracy was further improved by using the Lung Cancer Prediction convolutional neural network, an artificial intelligence-based model which obviates the requirement for nodule measurement.

• Nodule size and morphology are important factors in artificial intelligence lung cancer risk prediction, with nodule texture and background parenchyma contributing a small, but measurable, role.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08635-4.

Extending the value of routine lung screening CT with quantitative body composition assessment

Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with incidental lung cancer risk in the screening population is still unclear. We study the feasibility of body composition analysis using chest low dose computed tomography (LDCT). A two-stage fully automatic pipeline is developed to assess the cross-sectional area of body composition components including subcutaneous adipose tissue (SAT), muscle, visceral adipose tissue (VAT), and bone on T5, T8 and T10 vertebral levels. The pipeline is developed using 61 cases of the VerSe'20 dataset, 40 annotated cases of NLST, and 851 inhouse screening cases. On a test cohort consisting of 30 cases from the inhouse screening cohort (age 55 - 73, 50% female) and 42 cases of NLST (age 55 - 75, 59.5% female), the pipeline achieves a root mean square error (RMSE) of 7.25 mm (95% CI: [6.61, 7.85]) for the vertebral level identification and mean Dice similarity score (DSC) 0.99 ± 0.02, 0.96 ± 0.03, and 0.95 ± 0.04 for SAT, muscle, and VAT, respectively for body composition segmentation. The pipeline is generalized to the CT arm of the NLST dataset (25,205 subjects, 40.8% female, 1,056 lung cancer incidences). Time-to-event analysis for lung cancer incidence indicates inverse association between measured muscle cross-sectional area and incidental lung cancer risks (p < 0.001 female, p < 0.001 male). In conclusion, automatic body composition analysis using routine lung screening LDCT is feasible.

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography

PURPOSE

To establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT).

METHOD

A total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility.

RESULTS

For the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83-0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P<0.01; 87 vs. 61%, P<0.01; 85 vs. 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all).

CONCLUSION

The CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.

Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes

OBJECTIVES

To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size.

METHODS

We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm < diameter ≤ 2.0 cm), and T1c (2.0 cm < diameter ≤ 3.0 cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25 mm sharp), setting 2 (5 mm sharp), setting 3 (5 mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets.

RESULTS

The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models.

CONCLUSIONS

For PNs less than 1 cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1 cm, CT reconstruction parameters did not affect diagnostic performance substantially.

KEY POINTS

• CT image acquisition parameters have a significant influence on the diagnostic performance of radiomics in pulmonary nodules less than 1 cm. • In pulmonary nodules less than 1 cm, a radiomics model created by using images reconstructed with thin section and a sharp kernel algorithm achieved the best diagnostic performance. • For PNs larger than 1 cm, CT image acquisition parameters do not affect diagnostic performance substantially.

Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study

Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved.

Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

BACKGROUND

The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT).

PATIENTS AND METHODS

Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic ¹⁸F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUV_max) was measured on conventional ¹⁸F-FDG PET/CT images. The influx constant (K_i ) was calculated from the dynamic ¹⁸F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test.

RESULTS

Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUV_max values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average K_i values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min^-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613).

CONCLUSIONS

Dynamic ¹⁸F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.

Pre-Processing Method for Contouring the Uptake Levels of [18F] FDG for Enhanced Specificity of PET Imaging of Solitary Hypermetabolic Pulmonary Nodules

BACKGROUND

The paper presents a pre-processing method which, based on positron-emission tomography (PET) images of ¹⁸F-fluorodeoxyglucose ([18F] FDG) hypermetabolic pulmonary nodules, makes it possible to obtain additional visual characteristics and use them to enhance the specificity of imaging.

MATERIAL AND METHODS

A retrospective analysis of 69 FDG-PET/CT scans of solitary hypermetabolic pulmonary nodules (40 cases of lung cancer and 29 benign tumours), where in each case, the standardised uptake value of the hottest voxel within the defined volume of interest was greater than 2.5 (SUVmax > 2.5). No diagnosis could be made based on these SUVmax values. All of the PET DICOM images were transformed by means of the pre-processing method for contouring the uptake levels of [18F] FDG (PCUL-FDG). Next, a multidimensional comparative analysis was conducted using a synthetic variable obtained by calculating the similarities based on the generalised distance measure for non-metric scaling (GDM2) from the pattern object. The calculations were performed with the use of the R language.

RESULTS

The PCUL-FDG method revealed 73.9% hypermetabolic nodules definitively diagnosed as either benign or malignant lesions. As for the other 26.1% of the nodules, there was uncertainty regarding their classification (some had features suggesting malignancy, while the characteristics of others made it impossible to confirm malignancy with a high degree of certainty).

CONCLUSIONS

Application of the PCUL-FDG method enhances the specificity of PET in imaging solitary hypermetabolic pulmonary nodules. Images obtained using the PCUL-FDG method can serve as point of departure for automatic analysis of PET data based on convolutional neural networks.