Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Usuda, Katsuo; Iwai, Shun; Yamagata, Akira; Iijima, Yoshihito; Motono, Nozomu; Matoba, Munetaka; Doai, Mariko; Hirata, Keiya; Uramoto, Hidetaka

doi:10.3390/cancers13112720

Cited by 7 publications

(4 citation statements)

References 39 publications

(45 reference statements)

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“…Using single-section ROI method and whole-lesion histogram analysis, prior studies have consistently demonstrated significantly lower ADC mean and D mean values in malignant lung tumors than benign lesions ( 3 , 6 , 7 , 9 , 13 , 17 – 19 ). In contrast, another study based on whole-lesion histogram analysis revealed significantly higher ADC mean and ADC median values in lung cancer compared with benign inflammatory lesions ( 20 ). This opposite result may be attributed to the selection of different benign lung lesions, as they included lung cancer and specific infectious lesions (pulmonary abscesses and mycobacterium infections) for comparison.…”

Section: Discussionmentioning

confidence: 85%

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

Huang

et al. 2023

Front. Oncol.

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BackgroundWhole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions.PurposeTo compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis.MethodsFifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (Kapp) and kurtosis-corrected diffusion coefficient (Dapp) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance.ResultsThe ADCmean, ADCmedian, Dmean and Dmedian values of lung cancer were significantly lower than those of inflammatory lesions, while the ADCskewness, Kappmean, Kappmedian, KappSD, Kappkurtosis and Dappskewness values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADCskewness (p = 0.019) and Dmedian (p = 0.031) were identified as independent predictors of lung cancer. Dmedian showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a Dmedian of 1.091 × 10-3 mm2/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively.ConclusionsWhole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and Dmedian shows the best performance in the differential diagnosis of solitary pulmonary lesions.

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Section: Discussionmentioning

confidence: 85%

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

Huang

et al. 2023

Front. Oncol.

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“…For usefulness of BD score, Usuda et al reported that ADC histograms using BD score could discriminate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers, which discrimination was usually difficult in CT, PET-CT or MRI. ADC histogram has the potential to be a valuable tool to differentiate benignity from malignancy [32].…”

Section: Discussionmentioning

confidence: 99%

Determination of Treatment Efficacy of Lung Cancer using Diffusion-Weighted Whole Body Imaging with Background Suppression（DWIBS）Quantification Software (BD Score)

Yamamoto¹,

Numa²,

Matsuda³

et al. 2023

Preprint

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Chemotherapy for lung cancer has made remarkable progress, and its selection has been subdivided by genetic analysis. Although response evaluation criteria in solid tumors (RECIST) is a simple and highly objective evaluation method, it has been pointed out that it has the disadvantage of not being able to accurately evaluate the therapeutic effect of molecularly targeted drugs and immune checkpoint inhibitors. The purpose of this study is to determine whether quantitative evaluation of DWIBS is useful in determining the effectiveness of treatment for lung cancer. There were 31 patients with lung cancer and 56 patterns were obtained. First, the determination of treatment effect (PD, SD, PR) based on RECIST was performed on CT. Second, tDV and ADC (median) were measured using BD score from DWI images taken at the same time, and the rate of change was calculated. Then, we compared and examined the correlation between RECIST and the rate of change in tDV. There were correlations between RECIST and the rate of change in tDV in PD and PR cases respectively. Compared RECIST on CT, DWIBS using BD score would be more accurate for response evaluation of treatment. There was a tendency that tDV decreased with increasing ADC values, but some of the cases had a dissociated response with increasing ADC values and increasing tDV. Thus, since the color display of ADC values allows us to infer their contents, it would be useful for the evaluation of cases such as dissociated response and pseudoprogression, which are suspected to be atypical responses that are difficult to evaluate with RECIST and other methods.

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“…In MRI, there is also the possibility that radiomics is useful for diagnosis, prognosis, and response to treatment of lung cancer. Concerning the use of radiomics in the differential diagnosis between benign and malignant lung nodules, ADC histograms of PNMs are efficient methods for differential diagnosis [53].…”

Section: Discussionmentioning

confidence: 99%

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

Usuda

Ishikawa

Iwai

et al. 2021

Cancers

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The purpose of this retrospective study was to compare the diagnostic efficacy of FDG-PET/CT and MRI in discriminating malignant from benign pulmonary nodules and masses (PNMs). There were 278 lung cancers and 50 benign PNMs that were examined by FDG-PET/CT and MRI. The T2 contrast ratio (T2 CR) was designated as the ratio of T2 signal intensity of PNM divided by T2 signal intensity of the rhomboid muscle. The optimal cut-off values (OCVs) for differential diagnosis were 3.605 for maximum standardized uptake value (SUVmax), 1.459 × 10−3 mm2/s for apparent diffusion coefficient (ADC), and 2.46 for T2 CR. Areas under the receiver operating characteristics curves were 67.5% for SUVmax, 74.3% for ADC, and 72.4% for T2 CR, respectively. The sensitivity (0.658) of SUVmax was significantly lower than that (0.838) of ADC (p < 0.001) and that (0.871) of T2 CR (p < 0.001). The specificity (0.620) of SUVmax was that the same as (0.640) ADC and (0.640) of T2 CR. The accuracy (0.652) of SUVmax was significantly lower than that (0.808) of ADC (p < 0.001) and that (0.835) of T2 CR (p < 0.001). The sensitivity and accuracy of DWI and T2WI in MRI were significantly higher than those of FDG-PET/CT. Ultimately, MRI can replace FDG PET/CT for differential diagnosis of PNMs saving healthcare systems money while not sacrificing the quality of care.

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Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Cited by 7 publications

References 39 publications

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

Determination of Treatment Efficacy of Lung Cancer using Diffusion-Weighted Whole Body Imaging with Background Suppression（DWIBS）Quantification Software (BD Score)

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

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