Potential of spectral imaging generated by contrast-enhanced dual-energy CT for lung cancer histopathological classification - A preliminary study

Purpose

The potential of spectral images, particularly electron density and effective Z-images, generated by dual-energy computed tomography (DECT), for the histopathologic classification of lung cancer remains unclear. This study aimed to explore which imaging factors could better reflect the histopathological status of lung cancer.

Method

The data of 31 patients who underwent rapid kV-switching DECT and subsequently underwent surgery for lung cancer were analyzed. Virtual monochromatic images (VMIs) of 35 keV and 70 keV, virtual non-contrast images (VNC), iodine content images, electron density images, and effective Z-images were reconstructed for the following analyses: 1) correlation with the ratio of the lepidic growth pattern in the whole tumor and 2) comparisons with the four histological groups: well-differentiated adenocarcinoma (WDA), moderately differentiated adenocarcinoma (MDA), and poorly differentiated adenocarcinoma (PDA) and squamous cell carcinoma (SCC).

Results

There were significant correlations between the ratio of lepidic growth pattern and 70 keV, 35 keV, VNC, and electron density images (r = -0.861, P < 0.001; r = -0.791, P < 0.001; r = -0.869, P < 0.001; r = -0.871, P < 0.001, respectively). There were significant differences in the 70 keV, 35 keV, VNC, and electron density images in the Kruskal-Wallis test (P = 0.001, P = 0.006, P < 0.001, P < 0.001, respectively). However, there were no significant differences in iodine content or effective Z-images.

Conclusions

Electron density images generated by spectral imaging may be better indicators of the histopathological classification of lung cancer.

Clinical relevance

Electron density images may have an added value in predicting the histopathological classification of lung cancer.

Key points

•The role of electron density and effective Z-images obtained using dual-energy CT in lung cancer classification remains unclear.•Electron density and virtual non-contrast images correlated better with the ratio of lepidic growth patterns in lung cancer.•Electron density imaging is a better indicator of the histopathological classification of lung cancer than effective Z-imaging.

Insights into Radiology Publications

The evolution of modern medicine has been significantly driven by medical and health care research, underscoring the importance of disseminating findings to advance health care. Medical literature, encompassing various publication types such as case reports, review articles, and original research, plays a crucial role in this process by facilitating the communication and discussion of new discoveries. This review article provides a comprehensive guide to understanding and navigating radiologic publications. It examines the various types of radiologic research articles, including case reports and series, pictorial reviews, original research, systematic reviews, and meta-analyses, each of which serve distinct purposes in contributing to the field of radiology. The study adopts the "six honest men" approach-addressing why, who, what, when, where, and how-to elucidate the essential elements of successful radiology research and publication. Key topics include the motivations for publishing, the types of articles suited for different research questions, and strategic considerations for selecting appropriate journals. Additionally, the review highlights the importance of understanding publication timing, journal selection criteria, and the overall publication process, including manuscript preparation and peer review. By offering these insights, the review aims to equip early-career researchers with the knowledge and skills necessary to effectively contribute to radiology literature and advance their academic and professional careers.

Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas

The aim of the present study was to develop and evaluate a clinical-imaging-radiomic nomogram based on pre-enhanced computed tomography (CT) for pre-operative differentiation lipid-poor adenomas (LPAs) from metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas (AIs). A total of 196 consecutive patients with lung cancer, who underwent initial chest or abdominal pre-enhanced CT scan with small hyperattenuating AIs, were included. The patients were randomly divided into a training cohort with 71 cases of LPAs and 66 cases of metastases, and a testing cohort with 31 cases of LPAs and 28 cases of metastases. Plain CT radiological and clinical features were evaluated, including sex, age, size, pre-enhanced CT value (CTpre), shape, homogeneity and border. A total of 1,316 radiomic features were extracted from the plain CT images of the AIs, and the significant features selected by the least absolute shrinkage and selection operator were used to establish a Radscore. Subsequently, a clinical-imaging-radiomic model was developed by multivariable logistic regression incorporating the Radscore with significant clinical and imaging features. This model was then presented as a nomogram. The performance of the nomogram was assessed by calibration curves and decision curve analysis (DCA). A total of 4 significant radiomic features were incorporated in the Radscore, which yielded notable area under the receiver operating characteristic curves (AUCs) of 0.920 in the training dataset and 0.888 in the testing dataset. The clinical-imaging-radiomic nomogram incorporating the Radscore, CTpre, sex and age revealed favourable differential diagnostic performance (AUC: Training, 0.968; testing, 0.915) and favourable calibration curves. The nomogram was revealed to be more useful than the Radscore and the clinical-imaging model in clinical practice by DCA. The clinical-imaging-radiomics nomogram based on initial plain CT images by integrating the Radscore and clinical-imaging factors provided a potential tool to effectively differentiate LPAs from metastases in patients with lung cancer with small hyperattenuating AIs.

Can MDCT Enhancement Patterns Be Helpful in Differentiating Secretory from Non-Functional Adrenal Adenoma?

Background and Objectives: Primary adrenal tumors (AT) are a heterogeneous group of neoplasms due to their functional heterogeneity, which results in the diverse clinical presentation of these tumors. The purpose of this study was to examine cross-sectional imaging characteristics using multi-detector computed tomography (MDCT) to provide insight into the lesion characterization and functional status of these tumors. The radionuclide imaging using Technetium-99m radiolabeled hydrazinonicotinylacid-d-phenylalanyl1-tyrosine3-octreotide (99mTc-HYNIC-TOC), was also used in the diagnostic evaluation of these tumors. Materials and Methods: This cross-sectional study included 50 patients with confirmed diagnoses of AT (21 hormone-secreting and 29 non-functional) at the University Clinical Center, Kragujevac, Serbia, during the 2019-2022 year period. The morphological and dynamic characteristics using MDCT were performed, using qualitative, semi-quantitative, and quantitative analysis. Absolute washout (APW) and relative washout (RPW) values were also calculated. A semi-quantitative analysis of all visual findings with 99mTc-HYNIC-TOC was performed to compare the tumor to non-tumor tracer uptake. Results: A statistically significant difference was found in the MDCT values in the native phase (p < 0.05), the venous phase (p < 0.05), and the delayed phase (p < 0.001) to detect the existence of adrenal tumors. Most of these functional adrenocortical lesions (n = 44) can be differentiated using the delayed phase (p < 0.05), absolute percentage washout (APW) (p < 0.05), and relative percentage washout (RPW) (p < 0.001). Furthermore, 99mTc-HYNIC-TOC could have a high diagnostic yield to detect adrenal tumor existence (p < 0.001). There is a positive correlation between radionuclide imaging scan and APW to detect all AT (p < 0.01) and adrenocortical adenomas as well (p < 0.01). Conclusions: The results can be very helpful in a diagnostic algorithm to quickly and precisely diagnose the expansive processes of the adrenal glands, as well as to learn about the advantages and limitations of the mentioned imaging modalities.

Surgical Treatment of Solitary Metachronous Adrenal Metastasis from Urothelial Carcinoma of the Urinary Bladder

Urothelial cancer is a common neoplasm and metastatic disease correlates with a poor prognosis. Isolated adrenal gland metastases of urothelial carcinoma are quite rare, and management options can decide a patient's prognosis. Herein we report the case of a 76-year-old man with a metachronous solitary adrenal metastasis from a bladder carcinoma, who underwent adrenalectomy as part of his treatment. Furthermore, we discuss the cases of solitary adrenal metastases of urothelial carcinoma available in the literature, to identify key features to direct appropriate treatment of this rare metastatic site of urothelial cancer and improve prognosis and survival. Still, further prospective studies are needed to design effective therapeutic strategies.

Artificial intelligence in adrenal imaging: A critical review of current applications

In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.

Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article

The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-operative differentiation of "early stage" adrenal metastases from lipid-poor adenomas (LPAs). This retrospective study included 188 patients who underwent abdominal CECT (training cohort: LPAs, 68; metastases, 64; validation cohort: LPAs, 29; metastases, 27). Abdominal CECT included plain, arterial, portal, and venous imaging. Clinical and CECT radiological features were assessed and significant features were selected. Radiomic features of the adrenal lesions were extracted from four-phase CECT images. Significant radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. The clinical-radiological, unenhanced radiomics, arterial radiomics, portal radiomics, venous radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher than those of the clinical-radiological model (0.788) in the testing dataset. The AUC of the combined radiomics model (incorporating plain and venous radiomics features) was further improved to 0.953, which was significantly higher than portal radiomics model (P = .033) and clinical-radiological model (P = .009), with the highest accuracy (89.13%) and a relatively stable sensitivity (91.67%) and specificity (86.36%). As the optimal model, the combined radiomics model based on biphasic CT images is effective enough to differentiate "early stage" adrenal metastases from LPAs by reducing the radiation dose.

Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis

Objectives

(1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.

Methods

PubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman’s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks’ funnel plot.

Results

Twenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93–0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85–0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.

Conclusions

The methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = -5-8) and 6% (IQR = 0-22%), respectively. The highest and lowest scores registered were 12/36 (33%) and -5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.

Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art

Simple Summary

Non-invasive characterization of adrenal lesions requires a rigorous approach. Although CT is the cornerstone of adrenal lesion characterization, a multimodality multiparametric imaging approach helps improve confidence in adrenal lesion characterization.

Abstract

Detection and characterization of adrenal lesions have evolved during the past two decades. Although the role of imaging in adrenal lesions associated with hormonal secretion is usually straightforward, characterization of non-functioning adrenal lesions may be challenging to confidently identify those that need to be resected. Although many adrenal lesions can be readily diagnosed when they display typical imaging features, the diagnosis may be challenging for atypical lesions. Computed tomography (CT) remains the cornerstone of adrenal imaging, but other morphological or functional modalities can be used in combination to reach a diagnosis and avoid useless biopsy or surgery. Early- and delayed-phase contrast-enhanced CT images are essential for diagnosing lipid-poor adenoma. Ongoing studies are evaluating the capabilities of dual-energy CT to provide valid virtual non-contrast attenuation and iodine density measurements from contrast-enhanced examinations. Adrenal lesions with attenuation values between 10 and 30 Hounsfield units (HU) on unenhanced CT can be characterized by MRI when iodinated contrast material injection cannot be performed. ¹⁸F-FDG PET/CT helps differentiate between atypical benign and malignant adrenal lesions, with the adrenal-to-liver maximum standardized uptake value ratio being the most discriminative variable. Recent studies evaluating the capabilities of radiomics and artificial intelligence have shown encouraging results.

Comparison of MRI features in lipid-rich and lipid-poor adrenal adenomas using subjective and quantitative analysis

OBJECTIVE

To compare MR-imaging features in benign lipid-rich and lipid-poor adrenal adenomas.

MATERIALS AND METHODS

With institutional review board approval, we compared 23 consecutive lipid-poor adenomas (chemical shift [CS] signal intensity [SI] index < 16.5%) imaged with MRI to 29 consecutive lipid-rich adenomas (CS-SI index ≥ 16.5%) imaged during the same time period. A blinded radiologist measured T2-weighted (T2W) SI ratio (adrenal adenoma/psoas muscle), dynamic enhancement wash-in (WI) and wash-out (WO) indices, and T2W texture features. Two blinded Radiologists (R1/R2) assessed T2W-SI (relative to renal cortex) and T2W heterogeneity (using 5-Point Likert scales). Comparisons were performed between groups using independent t tests and Chi-square with Holm-Bonferroni correction.

RESULTS

There was no difference in age or gender between groups (p = 0.594, 0.051 respectively). Subjectively, all lipid-rich and lipid-poor adenomas were rated hypointense or isointense compared to renal cortex and T2W-SI did not differ between groups (p = 0.129, 0.124 for R1, R2). Agreement was substantial (Kappa = 0.67). There was no difference in T2W SI ratio (1.8 ± 0.9 [0.5-4.3] lipid rich versus 2.2 ± 1.0 [0.6-4.3] lipid poor, p = 0.139). Enhancement WI and WO did not differ comparing lipid-rich and lipid-poor adenomas (p = 0.759, 0.422 respectively). There was no difference comparing lipid-rich and lipid-poor adenomas T2W heterogeneity judged subjectively (p = 0.695, 0.139 for R1, R2; Kappa = 0.19) or by texture analysis (entropy, kurtosis, skewness; p = 0.134-0.191) with all adenomas except for one rated as mostly or completely homogeneous.

CONCLUSIONS

There is no difference in T2W signal intensity, enhancement pattern or T2W heterogeneity judged subjectively or by quantitative texture analysis comparing lipid-poor and lipid-rich adrenal adenomas.

Technical and Interpretive Pitfalls in Adrenal Imaging

The adrenal gland may exhibit a wide variety of pathologic conditions. A number of imaging techniques can be used to characterize these, although it is not always possible to attain a definitive diagnosis radiologically. Incorrect diagnoses may be made if radiologists are not attentive to technical parameters and interpretive factors associated with adrenal gland imaging. Hence, an appreciation of the intricacies of adrenal imaging strategies and characterization is required; this can be aided by understanding the pitfalls of adrenal imaging. Technical pitfalls at CT may relate to the imaging parameters, including region of interest characteristics, tube voltage selection, and the timing of contrast material-enhanced imaging. With MRI, imaging acquisition technique and evaluation of the reference tissues used in chemical shift MRI are important considerations that can directly influence image interpretation. Interpretive errors may occur when evaluating adrenal washout at CT without considering other radiologic features, including the size of adrenal nodules, the presence of fat or calcification, the attenuation of nodules, and atypical imaging features. The characterization of an incidental adrenal lesion as benign or malignant does not end the role of the radiologist; consideration as to whether an adrenal lesion is associated with endocrine dysfunction is required. While imaging may not be optimal for establishing endocrine activity, there are imaging features from which radiologists may infer function. In cases of known endocrine activity, imaging can guide clinical management, including further investigations such as venous sampling. ^©RSNA, 2020.

Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish?

INTRODUCTION

Distant metastases are found in the many of patients with lung cancer at time of diagnosis. Several diagnostic tools are available to distinguish between metastatic spread and benign lesions in the adrenal gland. However, all require additional diagnostic steps after the initial CT. The purpose of this study was to evaluate if texture analysis of CT-abnormal adrenal glands on the initial CT correctly differentiates between malignant and benign lesions in patients with confirmed lung cancer.

MATERIALS AND METHODS

In this retrospective study 160 patients with endoscopic ultrasound-guided biopsy from the left adrenal gland and a contrast-enhanced CT in portal venous phase were assessed with texture analysis. A region of interest encircling the entire adrenal gland was used and from this dataset the slice with the largest cross section of the lesion was analyzed individually.

RESULTS

Several texture parameters showed statistically significantly difference between metastatic and benign lesions but with considerable between-groups overlaps in confidence intervals. Sensitivity and specificity were assessed using ROC-curves, and in univariate binary logistic regression the area under the curve ranged from 36 % (Kurtosis 0.5) to 69 % (Entropy 2.5) compared to 73 % in the best fitting model using multivariate binary logistic regression.

CONCLUSION

In lung cancer patients with abnormal adrenal gland at imaging, adrenal gland texture analyses appear not to have any role in discriminating benign from malignant lesions.

Management guidelines for stage III non-small cell lung cancer

Management of stage III non- small cell lung cancer (NSCLC) is very challenging due to being a group of widely heterogeneous diseases that require multidisciplinary approaches with timely and coordinated care. The standards of care had significant changes over the last couple of years because of the introduction of consolidation therapy with checkpoint inhibitor following concurrent chemo-radiotherapy and the evolving new role of tyrosine kinase inhibitors in the adjuvant setting. The manuscript presents evidence-based recommendations for the workup, staging, treatment and follow up of the various subtypes of stage III NSCLC. The guidelines were developed by experts in various fields of thoracic oncology and guidelines development. The guidelines consider the sequence of interventions and the role of each discipline in the management of the disease taking into account the recent development and included required resources to help physicians provide better care.

Utility of T2-weighted MRI to Differentiate Adrenal Metastases from Lipid-Poor Adrenal Adenomas

Purpose

To evaluate T2-weighted MRI features to differentiate adrenal metastases from lipid-poor adenomas.

Materials and Methods

With institutional review board approval, this study retrospectively compared 40 consecutive patients (mean age, 66 years ± 10 [standard deviation]) with metastases to 23 patients (mean age, 60 years ± 15) with lipid-poor adenomas at 1.5- and 3-T MRI between June 2016 and March 2019. A blinded radiologist measured T2-weighted signal intensity (SI) ratio (SI_nodule/SI_{psoas muscle}), T2-weighted histogram features, and chemical shift SI index. Two blinded radiologists (radiologist 1 and radiologist 2) assessed T2-weighted SI and T2-weighted heterogeneity using five-point Likert scales.

Results

Subjectively, T2-weighted SI (P < .001 for radiologist 1 and radiologist 2) and T2-weighted heterogeneity (P < .001, for radiologist 1 and radiologist 2) were higher in metastases compared with adenomas when assessed by both radiologists. Agreement between the radiologists was substantial for T2-weighted SI (Cohen κ = 0.67) and T2-weighted heterogeneity (κ = 0.62). Metastases had higher T2-weighted SI ratio than adenomas (3.6 ± 1.7 [95% confidence interval {CI}: 0.2, 8.2] vs 2.2 ± 1.0 [95% CI: 0.6, 4.3], P < .001) and higher T2-weighted entropy (6.6 ± 0.6 [95% CI: 4.9, 7.5] vs 5.0 ± 0.8 [95% CI: 3.5, 6.6], P < .001). At multivariate analysis, T2-weighted entropy was the best differentiating feature (P < .001). Chemical shift SI index did not differ between metastases and adenomas (P = .748). Area under the receiver operating characteristic curve (AUC) for T2-weighted SI ratio and T2-weighted entropy were 0.76 (95% CI: 0.64, 0.88) and 0.94 (95% CI: 0.88, 0.99). The logistic regression model combining T2-weighted SI ratio with T2-weighted entropy yielded AUC of 0.95 (95% CI: 0.91, 0.99) and did not differ compared with T2-weighted entropy alone (P = .268). There was no difference in logistic regression model accuracy comparing the data by either field strength, 1.5- or 3-T MRI (P > .05).

Conclusion

Logistic regression models combining T2-weighted SI and T2-weighted heterogeneity can differentiate metastases from lipid-poor adenomas. Validation of these preliminary results is required.Keywords: Adrenal, MR-Imaging, UrinarySupplemental material is available for this article.© RSNA, 2020.

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept

INTRODUCTION

To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool.

MATERIALS AND METHODS

This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features.

RESULTS

Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024).

DISCUSSION

Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.