Spin-echo and diffusion-weighted MRI in differentiation between progressive massive fibrosis and lung cancer

Whole-volume diffusion kurtosis magnetic resonance (MR) imaging histogram analysis of non-small cell lung cancer: correlation with histopathology and degree of tumor differentiation

AIMS

To evaluate the role of diffusion kurtosis imaging (DKI) histogram analysis in the characterization of non-small cell lung cancer (NSCLC) and to correlate DKI parameters with tumor cellularity.

MATERIALS AND METHODS

Sixty-four patients with pathologically diagnosed NSCLCs were evaluated by DKI on a 3-T scanner. Regions of interest (ROIs) were drawn on the map of b1000 manually. All NSCLCs were histologically graded according to the degree of tumor differentiation. Tumor cellularity was measured by the nuclear-to-cytoplasm (N/C) ratio and the number of tumor cell nuclei (NTCN), the expression of Ki-67 was detected using the streptavidin-peroxidase method. Histogram analysis was performed using voxel-based on raw data from each ROI.

RESULTS

NSCLCs were classified as grades 1, 2, and 3 according to differentiation degree. Histogram parameters of apparent diffusion coefficient (ADC) and DKI could discriminate between different grades of tumors (p<0.001). Receiver operating characteristic (ROC) curve analysis showed that Kapp 75th exhibited the best performance with an AUC of 0.936 and sensitivity/specificity of 95.74%/80% (p<0.001) in distinguishing grade 1 from grade 2, ADC mean exhibited the best performance with an AUC of 0.923 and sensitivity/specificity of 92.33%/86.67% (p<0.001) in distinguishing grade 2 from 3. N/C ratio and Ki-67 changed significantly with grade (p<0.01). Negative correlations were found between the ADC mean and the N/C ratio, Ki-67, Dapp mean and N/C ratio, whereas Kapp mean and N/C ratio, Ki-67 were positively correlated.

CONCLUSIONS

DKI histogram analysis could quantitatively characterize NSCLC with different grades by probing non-Gaussian diffusion properties related to changes in the tumor microenvironment or tissue complexities in the tumor.

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.

The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma

AIM

To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma.

MATERIALS AND METHODS

The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values.

RESULTS

Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z = 2.624; p=0.007, z = 2.679).

CONCLUSION

The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.

Ability of three-dimensional 3-Tesla ultrashort echo time magnetic resonance imaging to display the morphological characteristics of pulmonary nodules: a sensitivity analysis

Background

Radiation-free lung cancer screening will reduce/eliminate radiation exposure in the diagnosis and follow-up of patients with lung cancer.

Methods

This was a prospective study. The participants were recruited using convenience sampling. A total of 36 patients with pulmonary nodules [patients with single or multiple pulmonary nodules >4 and <30 mm on their pulmonary computed tomography (CT) scans] who were admitted to Fudan University Zhongshan Hospital's Xiamen branch were enrolled; they underwent a CT scan and a free-breathing ultrashort time-of-echo (UTE) sequence scan using a 3-Tesla (T) magnetic resonance imaging (MRI) scanner. The CT examinations were regarded as the reference standard. Patients had an interval time of <3 days between their CT and MRI examinations. Two reviewers with more than 10 years' experience in the diagnosis of pulmonary nodules identified the numbers and morphological features of the pulmonary nodules.

Results

Among the 36 patients, 46 nodules were detected on CT images, 45 of which were also detected on UTE images (a detection rate of 97.6%). The detection rate for lobulation using UTE-MRI was 96.9%; however, the difference compared with the rate for the CT images was not statistically significant (P>0.05). In terms of confounding lesions (confounding lesions indicate that the patient has malignant tumours, benign nodules or inoperable nodules), the UTE-MRI method had a higher detection rate than the CT method, and the difference was significant (P<0.05). The probability of malignant lesions was found to be higher in confounding lesions than in homogeneous lesions. In terms of pleural traction, the UTE-MRI method demonstrated a higher detection rate (120%) than the CT method, but the difference was not statistically significant (P>0.05). In terms of spiculation, the UTE-MRI method demonstrated a lower detection rate (81.8%) than the CT method, although the difference was not statistically significant (P>0.05).

Conclusions

Overall, 3-T UTE-MRI imaging has a high detection rate for pulmonary nodules >4 mm and is similar to that of conventional CT imaging. The method can be used for radiation-free lung cancer screening and follow-up examinations to reduce/eliminate both repeat CT examinations and radiation damage.

Free-breathing BLADE fat-suppressed T2 weighted turbo spin echo sequence for distinguishing lung cancer from benign pulmonary nodules or masses: A pilot study

OBJECTIVE

Diffusion Weighted Imaging (DWI) can be used to differentiate benign and malignant pulmonary nodules or masses, while T2WI is also of great value in the differential diagnosis of them. For example, T2WI can be used to differentiate abscess from lung cancer. The study aims to quantitatively evaluate the efficacy of free-breathing BLADE fat-suppressed T2 weighted turbo spin echo sequence (BLADE T2WI) for differentiating lung cancer (LC) and benign pulmonary nodule or mass (BPNM).

METHODS

A total of 291 patients with LC (197 males, 94 females; mean age 63.2 years) and 74 BPNM patients (53 males, 21 females; mean age 62.8 years) who underwent BLADE T2WI at 3-T MRI between November 2016 and May 2022were included in this retrospective study. Two radiologists independently blinded observed the MR images and measured the T2 contrast ratio (T2CR). Mann-Whitney U test was used to compare T2CR values between the two groups, ROC curves were used to evaluate the diagnostic efficacy of BLADE T2WI.

RESULTS

The two radiologists had good inter-observer consistency for T2CR (ICC = 0.958). The T2CR of BPNM was significantly higher than LC (all p < 0.001); the cut-off value of T2CR was 2.135, and the sensitivity, specificity, and accuracy of diagnosis were 75.6%, 63.5%, and 73.2%, respectively. Moreover, T2CR correctly diagnosed 220 LC cases (220/291 = 75.6%) and 47 BPNM cases (47/74 = 63.5%).

CONCLUSION

The T2CR value of MR non-enhanced BLADE T2WI can be easily obtained and can quantitatively distinguish BPNM from LC, thus avoiding misdiagnosis caused by lack of work experience.

Progressive massive fibrosis: An overview of the recent literature

This review provides an overview of literature addressing progressive massive fibrosis (PMF) from September 2009 to the present. Advances are described in understanding its pathophysiology, epidemiology of the occurrence of PMF and related conditions, the impact of PMF on pulmonary function, advances in imaging of PMF, and factors affecting progression of pneumoconiosis in dust-exposed workers to PMF. Basic advances in understanding the etiology of PMF are impeded by the lack of a well-accepted animal model for human PMF. Recent studies evaluating lung tissue samples and epidemiologic investigations support an important role for the silica component of coal mine dust in causing coal workers' pneumoconiosis and PMF in contemporary coal miners in the United States and for silica in causing silicosis and PMF in artificial stone workers throughout the world. Development of PMF is associated with substantial decline in pulmonary function relative to no disease or small opacity pneumoconiosis. In recent reports, computed tomography has had greater sensitivity for detecting PMF than chest x-ray. Magnetic resonance imaging shows promise in differentiating between PMF and lung cancer. Although PMF develops in dust-exposed workers without previously identified small opacity pneumoconiosis, the presence of small opacity pneumoconiosis increases the risk for progression to PMF, as does heavier dust exposure. Recent literature does not document any effective new treatments for PMF and new therapies to prevent and treat PMF are an important need.