Diffusion-weighted MRI in solitary pulmonary lesions: associations between apparent diffusion coefficient and multiple histopathological parameters

Amide proton transfer-weighted MRI in predicting pathological types of brain metastases in lung Cancer

Most brain metastases originate from lung cancer. The majority of cases of lung cancer can be categorized into squamous carcinoma and adenocarcinoma,necessitating distinct clinical treatments and yielding diverse prognoses.Therefore,accurate preoperative evaluation of pathological types through imaging techniques is essential. The objective of this study is to assess the capability of amide proton transfer-weighted(APTw) MRI in predicting the pathological types of brain metastases in lung cancer.Additionally,it seeks to evaluate whether APTw MRI can provide additional value to diffusion-weighted imaging(DWI) at MRI·In this study,a total of 32 participants(mean age,60 ± 9 years;14 men) underwent evaluation,comprising 9 with squamous carcinoma and 23 with adenocarcinoma.Interestingly,adenocarcinoma demonstrated elevated APTw values(2.70 ± 0.81% vs 1.82 ± 0.47%;P = 0.001) and a higher apparent diffusion coefficient(ADC) value(1.00 ± 0.40 × 10-3 mm2/s vs 0.77 ± 0.13 × 10-3 mm2/s;P<0.05) in comparison to squamous carcinoma. The area under the receiver operating characteristic curve(AUC) of APTw and ADC in distinguishing between squamous carcinoma and adenocarcinoma were found to be 0.84 and 0.63,respectively.Moreover,the combined area under the receiver operating characteristic curve of the two techniques is 0.84. Amide proton transfer-weighted has the potential to predict the pathological types of brain metastases in lung cancer.

Magnetic particle targeting for diagnosis and therapy of lung cancers

Over the past decade, the growing interest in targeted lung cancer therapy has guided researchers toward the cutting edge of controlled drug delivery, particularly magnetic particle targeting. Targeting of tissues by magnetic particles has tackled several limitations of traditional drug delivery methods for both cancer detection (e.g., using magnetic resonance imaging) and therapy. Delivery of magnetic particles offers the key advantage of high efficiency in the local deposition of drugs in the target tissue with the least harmful effect on other healthy tissues. This review first overviews clinical aspects of lung morphology and pathogenesis as well as clinical features of lung cancer. It is followed by reviewing the advances in using magnetic particles for diagnosis and therapy of lung cancers: (i) a combination of magnetic particle targeting with MRI imaging for diagnosis and screening of lung cancers, (ii) magnetic drug targeting (MDT) through either intravenous injection and pulmonary delivery for lung cancer therapy, and (iii) computational simulations that models new and effective approaches for magnetic particle drug delivery to the lung, all supporting improved lung cancer treatment. The review further discusses future opportunities to improve the clinical performance of MDT for diagnosis and treatment of lung cancer and highlights clinical therapy application of the MDT as a new horizon to cure with minimal side effects a wide variety of lung diseases and possibly other acute respiratory syndromes (COVID-19, MERS, and SARS).

Enhancing the differentiation of pulmonary lymphoma and fungal pneumonia in hematological patients using texture analysis in 3-T MRI

Objectives

To evaluate texture analysis in nonenhanced 3-T MRI for differentiating pulmonary fungal infiltrates and lymphoma manifestations in hematological patients and to compare the diagnostic performance with that of signal intensity quotients (“nonenhanced imaging characterization quotients,” NICQs).

Methods

MR scans were performed using a speed-optimized imaging protocol without an intravenous contrast medium including axial T2-weighted (T2w) single-shot fast spin-echo and T1-weighted (T1w) gradient-echo sequences. ROIs were drawn within the lesions to extract first-order statistics from original images using HeterogeneityCAD and PyRadiomics. NICQs were calculated using signal intensities of the lesions, muscle, and fat. The standard of reference was histology or clinical diagnosis in follow-up. Statistical testing included ROC analysis, clustered ROC analysis, and DeLong test. Intra- and interrater reliability was tested using intraclass correlation coefficients (ICC).

Results

Thirty-three fungal infiltrates in 16 patients and 38 pulmonary lymphoma manifestations in 19 patients were included. Considering the leading lesion in each patient, diagnostic performance was excellent for T1w entropy (AUC 80.2%; p < 0.005) and slightly inferior for T2w energy (79.9%; p < 0.005), T1w uniformity (79.6%; p < 0.005), and T1w energy (77.0%; p < 0.01); the best AUC for NICQs was 72.0% for T2NICQmean (p < 0.05). Intra- and interrater reliability was good to excellent (ICC > 0.81) for these parameters except for moderate intrarater reliability of T1w energy (ICC = 0.64).

Conclusions

T1w entropy, uniformity, and energy and T2w energy showed the best performances for differentiating pulmonary lymphoma and fungal pneumonia and outperformed NICQs. Results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.

Key Points

• Texture analysis in nonenhanced pulmonary MRI improves the differentiation of pulmonary lymphoma and fungal pneumonia compared with signal intensity quotients.

• T1w entropy, uniformity, and energy along with T2w energy show the best performances for differentiating pulmonary lymphoma from fungal pneumonia.

• The results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.