Heterogeneity analysis of diffusion-weighted MRI for prediction and assessment of microstructural changes early after one cycle of induction chemotherapy in nasopharyngeal cancer patients

Beigi, Manijeh; Kazerooni, Anahita Fathi; Safari, Mojtaba; Alamolhoda, Marzieh; Moghdam, M. Shojaee; Moghadam, S Naseri; Salighehrad, Hamidreza; Alipour, Ahmad

doi:10.1007/s11547-017-0808-9

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Besides mean/median ADC, a more thorough analysis of the assessment of histogram parameters including first-order statistics, such as standard deviation, coefficient of variation and quantiles, as well as higher order statistics, such as skewness and kurtosis, may improve the assessment of response to treatment [ 35 , 70 ]. In our study, both skewness and kurtosis of the ADC distribution decreased following combined BRAF and MEK inhibition in sensitive, but not in resistant, xenografts.…”

Section: Discussionmentioning

confidence: 99%

Imaging markers of response to combined BRAF and MEK inhibition in BRAF mutated vemurafenib-sensitive and resistant melanomas

et al. 2018

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A majority of patients with a V600x melanoma respond quickly to BRAF/MEK inhibition (BRAFi/MEKi) and have an obvious clinical benefit. Nearly all the patients after this initial phase will develop resistance. Therefore, non-invasive early markers of response/non-response are needed in order to identify those patients who, due to intrinsic or acquired resistance, do not respond to treatment and would be eligible for alternative treatments.The aim of this study was to investigate the value of magnetic resonance spectroscopy (1H-MRS) of choline and diffusion-weighted magnetic resonance imaging (DW-MRI) as early markers of response to BRAF inhibition (BRAFi) with vemurafenib alone or in combination with MEK inhibition (MEKi) with trametinib, in BRAFi-sensitive and BRAFi-resistant melanoma xenografts.Tumor response was significantly improved by the combination of BRAFi and MEKi, compared to BRAFi alone, only in sensitive xenografts; thus indicating that vemurafenib-resistant A375R xenografts were cross-resistant to the inhibition of MEK, as confirmed by immunohistochemistry analysis for phosphorylated ERK.In vivo 1H-MRS showed that in sensitive melanoma xenografts, a significant blockage of ERK phosphorylation, but not a decrease in cell proliferation, was required to affect total choline (tCho) levels, thus suggesting that tCho could serve as a pharmacodynamic (PD) marker for agents targeting the MAPK cascade. In addition, early effects of the combination therapy on tumor cellularity could be detected via DW-MRI. In particular, skewness and kurtosis of the apparent diffusion coefficient (ADC) distribution may be useful to detect changes in the diffusional heterogeneity that might not affect the global ADC value.

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Section: Discussionmentioning

confidence: 99%

Imaging markers of response to combined BRAF and MEK inhibition in BRAF mutated vemurafenib-sensitive and resistant melanomas

et al. 2018

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“…Five percentiles, (0.1, 0.25, 0.5, 0.75 and 0.9), were used to characterize the intensity distribution across the tumor volume, defined by a region-of-interest (ROI) for each component. ROI histogram values have previously been demonstrated as relevant descriptors of tumor structures [5,7,28,29]. Additionally, they are robust to outliers and imperfect image registrations as opposed to voxel-based values and the ROI mean.…”

Section: Tumor Heterogeneity Assessmentmentioning

confidence: 99%

“…This is due to the response to ionizing radiation at a cellular level occurring on a much shorter timescale (hours to days) than the volumetric response (McMahon and Prise 2019). With an early response assessment there is time to adjust the therapy plan to increase treatment efficacy or reduce unnecessary radiation (Padhani et al 2009, Beigi et al 2018, van Houdt et al 2021, Zhao et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI

Rahbek¹,

Mahmood²,

Tomaszewski³

et al. 2023

Phys. Med. Biol.

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Objective: In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators (MR-Linacs) it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization. Approach: Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing: T$_2$-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome. Main Results: The framework was able to classify the two pancreatic tumor types with an \textit{area under curve} AUC of 0.999, $P<0.001$ and predict the tumor volume change with a correlation coefficient of 0.513, $P=0.034$. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74, $P=0.065$. Significance: A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.

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“…Recent literature reports that DWI with ADC values can be regarded as a useful method in the diagnosis and quantitative measurement of neoplasms (41)(42)(43); in particular, several evidences reported significantly lower ADC values in malignancies than in benign lesions and normal tissues (44,45). The ADC value has also been used to predict nodal metastases in many different cancers and to differentiate malignant from benign lymph nodes in various body regions and organs, such as head and neck, uterine, and cervical cancers (46)(47)(48)(49)(50)(51). The possible functional mechanism is that high cellularity in lymph node metastases produced by the tumor cells, may decrease extracellular and intracellular spaces, and restrict water molecules mobility, finally leading to a reduction of ADC value (52).…”

Section: Dwi Of Lymph Node Metastases: Current Evidencesmentioning

confidence: 99%

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

et al. 2020

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The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients.

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Heterogeneity analysis of diffusion-weighted MRI for prediction and assessment of microstructural changes early after one cycle of induction chemotherapy in nasopharyngeal cancer patients

Abstract: Our results suggest that heterogeneity metrics extracted from ADC-maps in metastatic lymph nodes, before and after IC, can be used as supplementary IC response indicators.

Cited by 6 publications

References 16 publications

Imaging markers of response to combined BRAF and MEK inhibition in BRAF mutated vemurafenib-sensitive and resistant melanomas

Imaging markers of response to combined BRAF and MEK inhibition in BRAF mutated vemurafenib-sensitive and resistant melanomas

Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

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