Diffusion weighted magnetic resonance imaging (DW-MRI) as a non-invasive, tissue cellularity marker to monitor cancer treatment response

Fliedner, Frederikke P.; Engel, Trine Bjørnbo; Ali, Henrik H. El; Hansen, Anders Elias; Kjær, Andreas

doi:10.1186/s12885-020-6617-x

Cited by 29 publications

(22 citation statements)

References 37 publications

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“…Its apparent diffusion coefficient (ADC) value shows a quantitative parameter of the diffusion of water molecules in biological tissues, and is usually significantly lower in malignant tumors compared with normal tissues or benign lesions [12] . DWI holds great potential for monitoring treatment response in cancer patients shortly after initiation of radiotherapy [13] .…”

Section: Introductionmentioning

confidence: 99%

Differentiation between suture recurrence and suture granuloma after pulmonary resection for lung cancer by diffusion-weighted magnetic resonance imaging or FDG-PET / CT

Usuda

Iwai

Yamagata

et al. 2021

Translational Oncology

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There has been no publication which supports the usefulness of DWI differentiating for suture recurrence and suture granuloma after resection for lung cancer. We presented efficacy of DWI or FDG-PET/CT for an assessment of suture lesions after resection for lung cancer. Thirteen suture recurrences and 15 suture granulomas were examined. There were 24 adenocarcinomas and 4 squamous cell carcinomas, and 26 partial resections and 2 segmentectomies. The period of time (907±907 days) between surgery and suture recurrence was not significantly longer than that (546±547 days) between surgery and suture granuloma. Diffusion detectability scores (a 5-point scale) of suture recurrences was significantly higher than that of suture granulomas. The ADC value (1.35±0.24 × 10 −3 mm 2 /sec) of suture recurrences was significantly lower than that (1.85±0.60 × 10 −3 mm 2 /sec) of suture granulomas. The SUVmax (6.1 ± 5.0) of suture recurrences was not significantly higher than that (4.2 ± 2.5) of suture granulmas. The sensitivity of 85% (11/13) with DWI was not significantly higher than 69% (9/13) with FDG-PET/CT for suture recurrences. The specificity of 73% (11/15) with DWI was not significantly higher than the 60% (9/15) with FDG-PET/CT for suture granulomas. The accuracy of 79% (22/28) with DWI was not significantly higher than that of 64% (18/28) with FDG-PET/CT for suture recurrences and granulomas. DWI can differentiate suture granuloma from suture recurrence after resection of lung cancer. DWI is more useful than FDG-PET/CT for the differentiation between suture recurrence and suture granuloma after resection for lung cancer.

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Section: Introductionmentioning

confidence: 99%

Differentiation between suture recurrence and suture granuloma after pulmonary resection for lung cancer by diffusion-weighted magnetic resonance imaging or FDG-PET / CT

Usuda

Iwai

Yamagata

et al. 2021

Translational Oncology

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“…DWI possesses great potential for monitoring treatment response in cancer patients shortly after the initiation of radiotherapy [17]. Functional evaluation of DWI was more useful than that of CT for the response evaluation of chemotherapy and/or radiotherapy to recurrent tumors of the lung [18].…”

Section: Introductionmentioning

confidence: 99%

Relationships and Qualitative Evaluation between Diffusion-Weighted Imaging and Pathologic Findings of Resected Lung Cancers

Usuda

Iwai

Yamagata

et al. 2020

Cancers

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For detecting malignant tumors, diffusion-weighted magnetic resonance imaging (DWI) as well as fluoro-2-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT) are available. It is not definitive how DWI correlates the pathological findings of lung cancer. The aim of this study is to evaluate the relationships between DWI findings and pathologic findings. In this study, 226 patients with resected lung cancers were enrolled. DWI was performed on each patient before surgery. There were 167 patients with adenocarcinoma, 44 patients with squamous cell carcinoma, and 15 patients with other cell types. Relationships between the apparent diffusion coefficient (ADC) of DWI and the pathology were analyzed. When the optimal cutoff value (OCV) of ADC for diagnosing malignancy was 1.70 × 10−3 mm2/s, the sensitivity of DWI was 92.0% (208/226). The sensitivity was 33.3% (3/9) in mucinous adenocarcinoma. The ADC value (1.31 ± 0.32 × 10−3 mm2/s) of adenocarcinoma was significantly higher than that (1.17 ± 0.29 × 10−3 mm2/s) of squamous cell carcinoma (p = 0.012), or (0.93 ± 0.14 × 10−3 mm2/s) of small cell carcinoma (p = 0.0095). The ADC value (1.91 ± 0.36 × 10−3 mm2/s) of mucinous adenocarcinoma was significantly higher than that (1.25 ± 0.25 × 10−3 mm2/s) of adenocarcinoma with mucin and that (1.24 ± 0.30 × 10−3 mm2/s) of other cell types. The ADC (1.11 ± 0.26 × 10−3 mm2/s) of lung cancer with necrosis was significantly lower than that (1.32 ± 0.33 × 10−3 mm2/s) of lung cancer without necrosis. The ADC of mucinous adenocarcinoma was significantly higher than those of adenocarcinoma of other cell types. The ADC of lung cancer was likely to decrease according to cell differentiation decreasing. The sensitivity of DWI for lung cancer was 92% and this result shows that DWI is valuable for the evaluation of lung cancer. Lung cancer could be evaluated qualitatively using DWI.

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“…In differentiating malignant and benign breast lesions, the diagnostic performance of contrast-enhanced magnetic resonance imaging (MRI) with DWI is higher than that of contrast-enhanced MRI with time-intensity curves [ 8 ]. In addition, DWI also has the potential to monitor radiation-induced treatment response and neoadjuvant treatment response [ 9 , 10 ]. More importantly, DWI can be an alternative for breast cancer screening without contrast media [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Zhou

Liu

et al. 2020

BMC Cancer

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Background The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer. Methods Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups. Results A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79, 77, 88, 92 and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67. Conclusions The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

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Diffusion weighted magnetic resonance imaging (DW-MRI) as a non-invasive, tissue cellularity marker to monitor cancer treatment response

Cited by 29 publications

References 37 publications

Differentiation between suture recurrence and suture granuloma after pulmonary resection for lung cancer by diffusion-weighted magnetic resonance imaging or FDG-PET / CT

Differentiation between suture recurrence and suture granuloma after pulmonary resection for lung cancer by diffusion-weighted magnetic resonance imaging or FDG-PET / CT

Relationships and Qualitative Evaluation between Diffusion-Weighted Imaging and Pathologic Findings of Resected Lung Cancers

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

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