Comparison of Spectral and Perfusion Computed Tomography Imaging in the Differential Diagnosis of Peripheral Lung Cancer and Focal Organizing Pneumonia

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.

[A clinical-radiomics nomogram for differentiating focal organizing pneumonia and lung adenocarcinoma]

OBJECTIVE

To evaluate the performance of a clinical-radiomics model for differentiating focal organizing pneumonia (FOP) and lung adenocarcinoma (LUAD).

METHODS

We retrospectively analyzed the data of 60 patients with FOP confirmed by postoperative pathology at the First Medical Center of the Chinese PLA General Hospital from January, 2019 to December, 2022, who were matched with 120 LUAD patients using propensity score matching in a 1∶2 ratio. The independent risk factors for FOP were identified by logistic regression analysis of the patients' clinical data. The cohort was divided into a training set (144 patients) and a test set (36 patients) by random sampling. Python 3.7 was used for extracting 1835 features from CT image data of the patients. The radiographic features and clinical data were used to construct the model, whose performance was validated using ROC curves in both the training and test sets. The diagnostic efficacy of the model for FOP and LUAD was evaluated and a diagnostic nomogram was constructed.

RESULTS

Statistical analysis revealed that an history of was an independent risk factor for FOP (P=0.016), which was correlated with none of the hematological findings (P > 0.05). Feature extraction and dimensionality reduction in radiomics yielded 30 significant labels for distinguishing the two diseases. The top 3 most discriminative radiomics labels were GraylevelNonUniformity, SizeZoneNonUniformity and shape-Sphericity. The clinical-radiomics model achieved an AUC of 0.909 (95% CI: 0.855-0.963) in the training set and 0.901 (95% CI: 0.803-0.999) in the test set. The model showed a sensitivity of 85.4%, a specificity of 83.5%, and an accuracy of 84.0% in the training set, as compared with 94.7%, 70.6%, and 83.3% in the test set, respectively.

CONCLUSION

The clinical-radiomics nomogram model shows a good performance for differential diagnosis of FOP and LUAD and may help to minimize misdiagnosis-related overtreatment and improve the patients' outcomes.

Extracellular volume fraction as a potential predictor to differentiate lung cancer from benign lung lesions with dual-layer detector spectral CT

Background

Extracellular volume (ECV) fraction has been used in cardiovascular diseases, pancreatic fibrosis, and hepatic fibrosis. The diagnostic value of ECV for focal lung lesions remains to be explored. The aim of this study was to evaluate the feasibility of ECV derived from a dual-layer detector computed tomography (DLCT) to differentiate lung cancer (LC) from benign lung lesions (BLLs).

Methods

Retrospectively, 128 consecutive patients with pathologically confirmed LC (n=86) or BLLs (n=42) were included. Conventional computed tomography (CT) characteristics and spectral CT parameters were assessed. All patients' hematocrits were measured to correct contrast volume distributions in blood while calculating ECV. After performing logistic regression analysis, a conventional CT-based model (Model A), DLCT-based model (Model B), combined diagnostic models (Model C), and an ECV-based model (Model D) were developed. The diagnostic effectiveness of each model was examined using the receiver operating characteristic (ROC) curve and their corresponding 95% confidence intervals (CIs). The area under the curve (AUC) of each model was compared using the DeLong test.

Results

Certain conventional CT features (such as lesion size, lobulation, spiculation, pleural indentation, and enlarged lymph nodes) differed significantly between the LC and BLL groups (all P<0.05). Statistical differences were found in the following DLCT parameters (all P<0.05): effective atomic number (Zeff) (non-enhancement), electron density (ED) (non-enhancement), ECV, iodine concentration (IC), and normalized iodine concentration (NIC). Models A, B, C, and D had AUCs of 0.801 [95% confidence interval (CI): 0.721-0.866], 0.805 (95% CI: 0.726-0.870), 0.925 (95% CI: 0.865-0.964), and 0.754 (95% CI: 0.671-0.826), respectively. The AUC of Model D (ECV) showed no significant difference from that of Models A and B (DeLong test, P>0.05).

Conclusions

The ECV derived from DLCT may be a potential new method to differentiate LC from BLLs, broadening the scope of ECV in clinical research.

Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma

BACKGROUND

Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation.

METHODS

There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups.

RESULTS

There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p < 0.05) except for WC (p > 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p < 0.05). In the ROC analysis, IC performed the best in the identification of the poorly differentiated group and non-poorly differentiated group in VP (AUC = 0.729, Sensitivity = 0.829, and Specificity = 0.569 under the threshold of 21.08 mg/ml).

CONCLUSIONS

Quantitative parameters of spectral CT could offer supplemental information for the preoperative differential diagnosis of ESCC with different degrees of differentiation.

Can spectral computed tomography (CT) replace perfusion CT to assess the histological classification of non-small cell lung cancer?

Background

Non-small cell lung cancer (NSCLC) accounts for 80% of total lung cancer cases, it is necessary to distinguish the histological types of NSCLC. This study set out to investigate the correlation between spectral computed tomography (CT) and CT perfusion parameters in patients with NSCLC and to compare the differential diagnostic efficacy of these two imaging modalities for the histological classification of NSCLC.

Methods

A total of 62 eligible consecutive patients, including 32 with lung adenocarcinoma (LUAD) and 30 with lung squamous cell carcinoma (LUSC), who underwent "one-stop" spectral combined perfusion scan and pathologically confirmed NSCLC at Lanzhou University Second Hospital between September 2020 and December 2021 were prospectively enrolled. The spectral parameters of lesions in the arterial phase (AP) and venous phase (VP) [including iodine concentration (IC), effective atomic number (Zeff), CT40keV, and slope of the spectral curve (K70keV)] and perfusion parameters [blood flow (BF), blood volume (BV), surface permeability (PS), and mean transit time (MTT)] were assessed. Pearson or Spearman correlation analysis was performed to evaluate the correlation between the two imaging parameters, and the DeLong test was used to compare the diagnostic performance of the two imaging modalities.

Results

BV and BF were strongly correlated with spectral parameters CT40keV, IC, Zeff, and K70keV in the AP and VP (0.6

Conclusions

Spectral parameters are significantly correlated with perfusion parameters in NSCLC, and spectral CT has a better diagnostic efficacy than perfusion CT in differentiating the histological classification of NSCLC.

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Key Words

• Non-small cell lung cancer (NSCLC)
• perfusion CT
• spectral CT

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Affiliation(s)

Liangna Deng Second Clinical School, Lanzhou University, Lanzhou, China Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
Jingjing Yang Second Clinical School, Lanzhou University, Lanzhou, China Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
Tiezhu Ren Second Clinical School, Lanzhou University, Lanzhou, China Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
Mengyuan Jing Second Clinical School, Lanzhou University, Lanzhou, China Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
Tao Han Second Clinical School, Lanzhou University, Lanzhou, China Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
Bin Zhang Second Clinical School, Lanzhou University, Lanzhou, China Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
Junlin Zhou Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China

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ALK-positive lung adenocarcinoma in a patient with rheumatoid arthritis with long-term treatment for organizing pneumonia: A case report

RATIONALE

Rheumatoid arthritis (RA) causes inflammation in various organs including the lungs. Pulmonary manifestations include inflammation of the pleura, vasculature, airway, and parenchyma, including interstitial lung disease (ILD). RA-organizing pneumonia (OP) is the third most common cause of RA-ILD. Cases of OP coexisting/complicated with lung cancer have been reported. Therefore, lung cancer can represent a diagnostic challenge, especially in patients with underlying pulmonary diseases including OP.

PATIENT CONCERNS

An 81-year-old woman with a 12-year history of RA-OP underwent multiple transbronchial lung biopsies (TBLBs), all of which resulted in no malignant findings. She was treated with prednisolone (PSL) depending on the deteriorated infiltrations. At admission, chest computed tomography (CT) images showed exacerbation of left S8 consolidation on chest CT. Additionally, her RA activity was exacerbated, and PSL dose was increased to 30 mg/day, which resulted in improved dyspnea and consolidation. Accordingly, PSL dose was gradually decreased. However, 6 months later, when PSL dose was 11 mg/d, due to a worsening of consolidation and the joint symptoms of RA, PSL dose was increased to 20 mg/d and tacrolimus 2 mg/d was administered. 3 months after the increase in PSL dose, dyspnea improved and PSL dose was reduced to 15 mg/d; however, she was admitted to our hospital because of low back pain.

DIAGNOSIS

Spinal magnetic resonance imaging showed bone metastases in the third and fifth lumbar vertebrae, and lung cancer was suspected as the primary tumor on CT.

INTERVENTIONS

TBLB was performed on the left B8 infiltrate, which showed no evidence of malignancy in the previous TBLB.

OUTCOMES

Pathological examination of TBLB on the left B8 revealed an adenocarcinoma that was positive for anaplastic lymphoma kinase.

LESSONS

Physicians should be aware of the development of lung cancer in regions with OP, even after a partial response to corticosteroid therapy.