Diffusion MRI in early cancer therapeutic response assessment

Standardization and advancements efforts in breast diffusion-weighted imaging

Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost. Diffusion-weighted imaging (DWI), a non-contrast technique, has shown promise in differentiating malignant from benign lesions. It offers shorter scanning times and eliminates contrast agent risks. Apparent diffusion coefficient (ADC) values provide quantitative measures for lesion characterization, potentially reducing unnecessary biopsies. Studies have revealed some correlations between ADC values and hormone receptor status in breast cancers, although substantial variability exists among studies. However, standardization remains challenging. Initiatives such as European Society of Breast Imaging (EUSOBI), Diffusion-Weighted Imaging Screening Trial (DWIST), Quantitative Imaging Biomarkers Alliance (QIBA) have proposed guidelines to ensure consistency in imaging protocols and equipment specifications, addressing variability in ADC measurements across different sites and vendors. Advanced techniques like Intravoxel incoherent motion (IVIM) and non-Gaussian DWI offer insights into tissue microvasculature and microstructure. Despite ongoing challenges, the integration of these advanced MRI techniques shows great promise for improving breast cancer diagnosis, characterization, and treatment planning. Continued research and standardization efforts are crucial for maximizing the potential of breast DWI in enhancing patient care and outcomes.

Diagnostic performance of simultaneous multislice diffusion-weighted imaging in differentiating breast lesions: a systematic review and meta-analysis

OBJECTIVES

To investigate the diagnostic performance of simultaneous multislice diffusion-weighted imaging (SMS-DWI) in differentiating malignant and benign breast lesions, and compare it with conventional single shot and readout segmented echo planar imaging without the SMS technique.

METHODS

The literature search was performed in PubMed, Embase, and Web of Science to identify comparative studies reporting the diagnostic performance of SMS-DWI and conventional DWI in patients with breast lesions. Histopathological analysis was used as a reference standard for malignant breast lesions. The methodological quality was evaluated using QUADAS-2 scale. The summary sensitivity, summary specificity, and area under the curve (AUC) of the summarized receiver operating characteristic curve were calculated and compared between SMS-DWI and conventional DWI using a bivariate random-effects model. Heterogeneity was explored with meta-regression and subgroup analyses.

RESULTS

Six studies with 626 patients and 649 breast lesions (benign: 222, malignant: 427) were included. The summary sensitivity, summary specificity, and AUC for SMS-DWI were 0.89 (95% CI: 0.78-0.95), 0.94 (95% CI: 0.81-0.98), and 0.96 (95% CI: 0.94-0.98), respectively, and those for conventional DWI were 0.90 (0.95 CI: 0.84-0.94), 0.87 (95% CI: 0.80-0.92), and 0.94 (95% CI: 0.92-0.96), respectively. The diagnostic performance was not significantly different between SMS-DWI and conventional DWI (P = .337).

CONCLUSIONS

SMS-DWI has high diagnostic performance in differentiating breast lesions, which is not significantly different from the conventional DWI.

ADVANCES IN KNOWLEDGE

There is no significant difference between SMS-DWI and conventional DWI in differentiating breast lesions, suggesting SMS-DWI may be a potential alternative to conventional DWI in breast imaging.

Lesion Volume Divided by ADC Measures Is an Independent Prognostic Marker in Colorectal Liver Metastasis Treated by Y90-radioembolization

BACKGROUND/AIM

To assess the ability of apparent diffusion coefficient (ADC) at baseline in predicting overall survival in patients who undergo Y90-radioembolization (Y90-RE) for liver-dominant metastatic colorectal cancer (mCRC) in the salvage situation.

PATIENTS AND METHODS

A retrospective review of 411 lesions in 63 patients with refractory mCRC treated with Y90-RE was conducted. Manual region of interest (ROI) measurements were applied using a whole lesion and volume method. Minimum and mean ADC values were measured, and averages were calculated per patient. Ratios combining tumor volume and ADC were correlated with OS, and a receiver-operating characteristic (ROC) analysis defined a cut-off value. Cox regression analysis was performed, and the log-rank test confirmed prognostic cut-off levels for survival.

RESULTS

The median survival was 6.4 months. Multivariate Cox regression identified tumor volume divided by minimum ADC (ADCtumor volume, min) as an independent predictor of OS (HR=1.814, 95%CI=1.188-2.770, p=0.006). Neither ADCmin nor ADCmean were significantly associated with survival. Optimal prediction was identified with a ADCtumor volume, min cut-off of 0.3673 arbitrary units (AU) yielding 76.0% sensitivity and 70.3% specificity. Patients with ADCtumor volume min <0.3673 had a median OS of 10.4 months, compared to 4.7 months for those above the cut-off (p<0.001).

CONCLUSION

Tumor volume divided by minimum ADC at baseline (ADCtumor volume, min) was identified as an independent predictor of OS in mCRC scheduled for Y90-radioembolization in the salvage situation and may improve future patient selection.

Unsupervised clustering analysis-based characterization of spatial profiles of inaccuracy in apparent diffusion coefficient values with varying acquisition plan orientation and diffusion weighting gradient direction - a large multicenter phantom study

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis. A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s mm-2) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function (ar2+ br + c). Moreover, for each Δ-profile, the maximum minus minimum of the fitting function (δmax) was calculated. The parametersa,b,c, andδmaxshowed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parametera(p< 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system. The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction.

Quantitative MRI Measurements Capture Pancreatic Cancer and Stroma Reactions to New KRAS Inhibitor

In pancreatic ductal adenocarcinoma (PDAC), KRAS mutations drive both cancer cell growth and formation of a dense stroma. Small molecule KRAS inhibitors (KRASi) represent a breakthrough for PDAC treatment hence clinical tools that can assess early response, detect resistance and/or predict prolonged survival are desirable for management of patients undergoing KRASi therapy. We hypothesized that diffusion-weighted MRI (DWI) can detect cell death while dynamic contrast enhanced MRI (DCE) and magnetization transfer ratio (MTR) imaging are sensitive to tumor microenvironment changes, and these metrics shed insights into tumor size (standard care assessment) change induced by KRASi treatment. We tested this hypothesis in multiple preclinical PDAC models receiving MRTX1133, an investigational new drug specific for KRAS G12D mutation. Quantitative imaging markers corroborated by immunohistochemistry (IHC) revealed significant and profound changes related to cell death accompanied by changes in tumor cellularity, capillary perfusion /permeability and stromal matrix as early as 48h and day-7 after initiation of KRASi treatment, and greatly prolonged median survival over controls in a genetic engineered mouse model of PDAC (KPC). The MRI markers also captured distinct responses to KRASi therapy from PDAC tumors carrying KRAS G12C versus KRAS G12D mutation. In tumors developed resistance to MRTX1133, the imaging markers exhibited a reversal from those of responding tumors. Our findings have established that multiparametric MRI provide biological insights including cell death, reduced cellularity and tumor microenvironment changes induced by KRASi treatment and set the stage for testing the utility of these clinically ready MRI methods in patients receiving KRASi therapy.

Translational relevance

Emerging small molecule KRAS inhibitors (KRASi) represent a new class of therapy for PDAC. Clinical tools that can provide biological insights beyond tumor size change are desirable for management of patients under KRASi therapy. DWI and DCE are frequently applied MRI methods for assessing cancer treatment responses in clinical trials. Using multiple PDAC models, we examined whether DWI, DCE and MTR can enhance the standard care assessment (tumor size) to MRTX1133, a KARSi with investigational new drug (IND) status. Our data demonstrate the abilities of DWI, DCE and MTR derived imaging markers to detect the early (48h) cell death, pronounced stromal changes and development of resistance to KRASi. This study has high translational relevance by testing clinically ready MRI methods, an IND and a genetic engineered mouse model that recapitulates saline features of human PDAC.

Filter-exchange spectroscopy is sensitive to gradual cell membrane degradation

Transmembrane water permeability changes occur after initialization of necrosis and are a mechanism for early detection of cell death. Filter-exchange spectroscopy (FEXSY) is sensitive to transmembrane water permeability and enables its quantification by magnetic resonance via the apparent exchange rate (AXR). In this study, we investigate AXR changes during necrotic cell death. FEXSY measurements of yeast cells in different necrotic stages were performed and compared with established fluorescence cell death markers and pulsed gradient spin echo measurements. Furthermore, the influence of T2 relaxation on AXR was examined in a two-compartment system. The AXR of yeast cells increased slightly after incubation with 20% isopropanol, whereas it peaked sharply after incubation with 25% isopropanol. At this point, almost all the yeast cells were vital but showed compromised membranes. After incubation with 30% isopropanol, AXR measurements showed high variability, at a point corresponding to a majority of the yeast cells being in late-stage necrosis with disrupted cell membranes. Simulations revealed that, for FEXSY measurements in a two-compartment system, a long filter echo time (TEf), compared with the T2 of the slow-diffusing compartment, filters out a fraction of the slow-diffusing compartment signal and leads to overestimation of apparent diffusion coefficient (ADC) and underestimation of AXR. Our results demonstrate that AXR is sensitive to gradual permeabilization of the cell membrane of living cells in different permeabilization stages without exogenous contrast agents. AXR measurements were sensitive to permeability changes induced by relatively low concentrations of isopropanol, at levels for which no measurable effect was detectable by ADC measurements. TEf may act as a signal filter that affects the estimated AXR value of a system consisting of a variety of local diffusivities and a range of T2 that includes T2 values shorter or comparable with the TEf.

Utility of interim apparent diffusion coefficient value in predicting treatment response among patients with locally advanced cervical cancer treated with radiotherapy

Background

For locally advanced cervical cancer (LACC), treatment response to radiotherapy (RT) can vary significantly even among those with the same stage classification of International Federation of Gynecology and Obstetrics (FIGO). This study investigated the value of ADC metric for forecasting end-of-treatment outcomes in LACC patients referred for RT.

Methods

Eighty patients with pathologically confirmed cervical squamous cell carcinoma with (SCC) were included in the research. Abdominal or pelvic MRI scans were conducted at least three times for all participants: before RT, three weeks after beginning of RT and approximately two months after RT was finalized. Calculated apparent diffusion coefficient (ADC) values of the LACC include: pre-ADC, interim-ADC, ΔADC and Δ%ADC. Based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, subjects were calculated and subsequently categorized into good responders group (complete response) and poor responders group (progressive disease, stable disease or partial response).

Results

Compared to good-responders, subjects of poor-responder group showed significantly lower values of interim-ADC, ΔADC, and Δ%ADC (all P < 0.05). To distinguish between good and poor responders, the optimal cutoff values of interim-ADC, ΔADC, and Δ%ADC were determined to be 1.067 × 10-3 mm2/sec, 0.209 × 10-3 mm2/sec, and 30.74 % using the ROC curve, with corresponding sensitivities of 83.78 %, 86.49 %, 75.68 %, and specificities of 88.37 %, 86.49 %, 75.68 %, respectively. Multivariate logistic regression revealed that the baseline tumor diameter and interim-ADC were significant prognostic factors for treatment response with an odds ratio (OR) of 0.105 (95 % confidence interval [95 % CI] 0.018-0.616) for baseline tumor diameter and 42.896 (95 % CI 8.205-224.262) for interim-ADC.

Conclusion

The interim-ADC value and baseline tumor diameter surfaced as possible indicative factors for predicting the response to RT in patients with LACC.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer

OBJECTIVE

Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features.

METHODS

This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS).

RESULTS

Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival.

CONCLUSION

Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early.

CRITICAL RELEVANCE STATEMENT

Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging.

KEY POINTS

Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery

Background and purpose

Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map ("forward models"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images ("inverse model"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS).

Materials and methods

Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients.

Results

Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model.

Conclusions

A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning

PURPOSE

The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

METHODS

Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature.

RESULTS

Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines.

CONCLUSIONS

Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Prediction of 177Lu-DOTATATE PRRT Outcome Using Multimodality Imaging in Patients with Gastroenteropancreatic Neuroendocrine Tumors: Results from a Prospective Phase II LUMEN Study

Our objective was to predict the outcome of peptide receptor radionuclide therapy (PRRT) using multimodality imaging and tumor dosimetry on gastroenteropancreatic neuroendocrine tumor (GEP-NET) lesions and patients. Methods: This prospective study included patients with progressive GEP-NETs. Treatment consisted of 4 cycles of 7.4 GBq of 177Lu-DOTATATE. Imaging parameters were measured on 68Ga-DOTATATE PET/CT (SUVmax/mean, somatostatin receptor [SSTR] tumor volume [TV], total lesion SSTR expression, and tumor-to-blood and tumor-to-spleen ratios), 18F-FDG PET/CT (SUVmax/mean, metabolically active TV, and total lesion glycolysis), and diffusion-weighted MRI (apparent diffusion coefficient) in a maximum of 5 target lesions per patient at approximately 10 wk after each injection. Tumor dosimetry was performed using SPECT/CT at 3 time points for every cycle. Baseline imaging parameters, their relative changes after PRRT cycle 1 (C1), and the tumor-absorbed dose at C1 were correlated with lesion morphologic outcome. The average values of the imaging parameters and the minimal, maximal, and mean C1 tumor-absorbed dose in each patient were tested for association with progression-free survival (PFS) and best objective response (RECIST 1.1). Results: In the 37 patients, the median PFS was 28 mo. Eleven of the 37 (30%) achieved a partial response (RECIST 1.1). After a median follow-up of 57 mo, the median time to lesion progression had not been reached in 84 morphologically evaluable lesions, with only 12 (14%) progressing (size increase ≥ 20% from baseline). Patients receiving a minimal C1 dose of 35 Gy in all target lesions exhibited a significantly longer PFS (48.1 vs. 26.2 mo; hazard ratio, 0.37; 95% CI, 0.17-0.82; P = 0.02). Volumetric 68Ga-DOTATATE PET parameters correlated with lesion and patient outcome: patients with an SSTR TV decrease of more than 10% after C1 had a longer PFS (51.3 vs. 22.8 mo; hazard ratio, 0.35; 95% CI, 0.16-0.75; P = 0.003). There was no statistical evidence of an association between other dosimetric or imaging parameters and the lesion or patient outcome. Conclusion: Minimal tumor-absorbed dose at C1 is predictive of outcome in patients with GEP-NETs treated with PRRT, providing a basis for personalized dosimetry-guided treatment strategies. An SSTR TV decrease after C1 could be used for early therapy response assessment as a predictor of PRRT outcome.

Viability of AMURA biomarkers from single-shell diffusion MRI in clinical studies

Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restrict the evaluation of complex structures. The objective of this study was to validate the reliability and robustness of complementary diffusion measures extracted with a novel approach, Apparent Measures Using Reduced Acquisitions (AMURA), with a typical diffusion MRI acquisition from a clinical context in comparison with DTI with application to clinical studies. Fifty healthy controls, 51 episodic migraine and 56 chronic migraine patients underwent single-shell diffusion MRI. Four DTI-based and eight AMURA-based parameters were compared between groups with tract-based spatial statistics to establish reference results. On the other hand, following a region-based analysis, the measures were assessed for multiple subsamples with diverse reduced sample sizes and their stability was evaluated with the coefficient of quartile variation. To assess the discrimination power of the diffusion measures, we repeated the statistical comparisons with a region-based analysis employing reduced sample sizes with diverse subsets, decreasing 10 subjects per group for consecutive reductions, and using 5,001 different random subsamples. For each sample size, the stability of the diffusion descriptors was evaluated with the coefficient of quartile variation. AMURA measures showed a greater number of statistically significant differences in the reference comparisons between episodic migraine patients and controls compared to DTI. In contrast, a higher number of differences was found with DTI parameters compared to AMURA in the comparisons between both migraine groups. Regarding the assessments reducing the sample size, the AMURA parameters showed a more stable behavior than DTI, showing a lower decrease for each reduced sample size or a higher number of regions with significant differences. However, most AMURA parameters showed lower stability in relation to higher coefficient of quartile variation values than the DTI descriptors, although two AMURA measures showed similar values to DTI. For the synthetic signals, there were AMURA measures with similar quantification to DTI, while other showed similar behavior. These findings suggest that AMURA presents favorable characteristics to identify differences of specific microstructural properties between clinical groups in regions with complex fiber architecture and lower dependency on the sample size or assessing technique than DTI.

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver

PURPOSE

To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques.

METHODS

DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference.

RESULTS

In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( p = 0 . 0078 $$ p=0.0078 $$ , reproducibility coefficients (RPC) =  470 × 1 0 - 6 $$ 470\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( p = 0 . 29 $$ p=0.29 $$ , RPC = 220 × 1 0 - 6 $$ \mathrm{RPC}=220\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( p < 0 . 001 $$ p<0.001 $$ ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI.

CONCLUSION

Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging-What can we do better?

PURPOSE

Diffusion-weighted MRI is a promising technique to monitor response to treatment in pediatric rhabdomyosarcoma. However, its validation in clinical practice remains challenging. This study aims to investigate how the tumor segmentation strategy can affect the apparent diffusion coefficient (ADC) measured in pediatric rhabdomyosarcoma.

MATERIALS AND METHODS

A literature review was performed in PubMed using search terms relating to MRI and sarcomas to identify commonly applied segmentation strategies. Seventy-six articles were included, and their presented segmentation methods were evaluated. Commonly reported segmentation strategies were then evaluated on diffusion-weighted imaging of five pediatric rhabdomyosarcoma patients to assess their impact on ADC.

RESULTS

We found that studies applied different segmentation strategies to define the shape of the region of interest (ROI)(outline 60%, circular ROI 27%), to define the segmentation volume (2D 44%, multislice 9%, 3D 21%), and to define the segmentation area (excludes edge 7%, excludes other region 19%, specific area 27%, whole tumor 48%). In addition, details of the segmentation strategy are often unreported. When implementing and comparing these strategies on in-house data, we found that excluding necrotic, cystic, and hemorrhagic areas from segmentations resulted in on average 5.6% lower mean ADC. Additionally, the slice location used in 2D segmentation methods could affect ADC by as much as 66%.

CONCLUSION

Diffusion-weighted MRI studies in pediatric sarcoma currently employ a variety of segmentation methods. Our study shows that different segmentation strategies can result in vastly different ADC measurements, highlighting the importance to further investigate and standardize segmentation.

Morpho-functional analysis of patient-derived xenografts reveals differential impact of gastric cancer and chemotherapy on the tumor ecosystem, affecting immune check point, metabolism, and sarcopenia

OBJECTIVES

Gastric cancer (GC) is an aggressive disease due to late diagnosis resulting from the lack of easy diagnostic tools, resistances toward immunotherapy (due to low PD-L1 expression), or chemotherapies (due to p53 mutations), and comorbidity factors, notably muscle atrophy. To improve our understanding of this complex pathology, we established patient-derived xenograft (PDX) models and characterized the tumor ecosystem using a morpho-functional approach combining high-resolution imaging with molecular analyses, regarding the expression of relevant therapeutic biomarkers and the presence of muscle atrophy.

MATERIALS AND METHODS

GC tissues samples were implanted in nude mice. Established PDX, treated with cisplatin or not, were imaged by magnetic resonance imaging (MRI) and analyzed for the expression of relevant biomarkers (p53, PD-L1, PD-1, HER-2, CDX2, CAIX, CD31, a-SAM) and by transcriptomics.

RESULTS

Three well-differentiated, one moderately and one poorly differentiated adenocarcinomas were established. All retained the architectural and histological features of their primary tumors. MRI allowed in-real-time evaluation of differences between PDX, in terms of substructure, post-therapeutic changes, and muscle atrophy. Immunohistochemistry showed differential expression of p53, HER-2, CDX2, a-SAM, PD-L1, PD-1, CAIX, and CD31 between models and upon cisplatin treatment. Transcriptomics revealed treatment-induced hypoxia and metabolic reprograming in the tumor microenvironment.

CONCLUSION

Our PDX models are representative for the heterogeneity and complexity of human tumors, with differences in structure, histology, muscle atrophy, and the different biomarkers making them valuable for the analyses of the impact of platinum drugs or new therapies on the tumor and its microenvironment.

Improved geometric accuracy of whole body diffusion-weighted imaging at 1.5T and 3T using reverse polarity gradients

Whole body diffusion-weighted imaging (WB-DWI) is increasingly used in oncological applications, but suffers from misalignments due to susceptibility-induced geometric distortion. As such, DWI and structural images acquired in the same scan session are not geometrically aligned, leading to difficulties in e.g. lesion detection and segmentation. In this work we assess the performance of the reverse polarity gradient (RPG) method for correction of WB-DWI geometric distortion. Multi-station DWI and structural magnetic resonance imaging (MRI) data of healthy controls were acquired at 1.5T (n = 20) and 3T (n = 20). DWI data was distortion corrected using the RPG method based on b = 0 s/mm² (b0) and b = 50 s/mm² (b50) DWI acquisitions. Mutual information  (MI) between low b-value DWI and structural data increased with distortion correction (P < 0.05), while improvements in region of interest (ROI) based similarity metrics, comparing the position of incidental findings on DWI and structural data, were location dependent. Small numerical differences between non-corrected and distortion corrected apparent diffusion coefficient (ADC) values were measured. Visually, the distortion correction improved spine alignment at station borders, but introduced registration-based artefacts mainly for the spleen and kidneys. Overall, the RPG distortion correction gave an improved geometric accuracy for WB-DWI data acquired at 1.5T and 3T. The b0- and b50-based distortion corrections had a very similar performance.

VERDICT MRI for radiation treatment response assessment in neuroendocrine tumors

Noninvasive methods to study changes in tumor microstructure enable early assessment of treatment response and thus facilitate personalized treatment. The aim of this study was to evaluate the diffusion MRI model, Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT), for early response assessment to external radiation treatment and to compare the results with those of more studied sets of parameters derived from diffusion-weighted MRI data. Mice xenografted with human small intestine tumors were treated with external radiation treatment, and diffusion MRI experiments were performed on the day before and up to 2 weeks after treatment. The diffusion models VERDICT, ADC, IVIM, and DKI were fitted to MRI data, and the treatment response of each tumor was calculated based on pretreatment tumor growth and post-treatment tumor volume regression. Linear regression and correlation analysis were used to evaluate each model and their respective parameters for explaining the treatment response. VERDICT analysis showed significant changes from day -1 to day 3 for the intracellular and extracellular volume fraction, as well as the cell radius index (p < 0.05; Wilcoxon signed-rank test). The strongest correlation between the diffusion model parameters and the tumor treatment response was seen for the ADC, kurtosis-corrected diffusion coefficient, and intracellular volume fraction on day 3 (τ = 0.47, 0.52, and -0.49, respectively, p < 0.05; Kendall rank correlation coefficient). Of all the tested models, VERDICT held the strongest explanatory value for the tumor treatment response on day 3 (R²  = 0.75, p < 0.01; linear regression). In conclusion, VERDICT has potential for early assessment of external radiation treatment and may provide further insights into the underlying biological effects of radiation on tumor tissue. In addition, the results suggest that the time window for assessment of treatment response using dMRI may be narrow.

Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma

RATIONALE AND OBJECTIVES

Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression.

MATERIALS AND METHODS

We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRM_rFLAIR+), decreased (PRM_rFLAIR-), or unchanged (PRM_rFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRM_rFLAIR between weeks 10 and 20 and continuously until the PRM_rFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection.

RESULTS

Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRM_rFLAIR+ (PFS: p <0.01; OS: p <0.001). PRM_rFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRM_rFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001).

CONCLUSION

PRM_rFLAIR may provide an early biomarker of disease progression in glioblastoma.

Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI

Background

This study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.

Methods

Fifty patients (mean age, 48.7 years) with stage II–III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed.

Results

Clinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021–0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024–95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05).

Conclusions

Our findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-021-00688-z.

Phase I/II Study of LDE225 in Combination with Gemcitabine and Nab-Paclitaxel in Patients with Metastatic Pancreatic Cancer

BACKGROUND

Desmoplasia is a central feature of the tumor microenvironment in pancreatic ductal adenocarcinoma (PDAC). LDE225 is a pharmacological Hedgehog signaling pathway inhibitor and is thought to specifically target tumor stroma. We investigated the combined use of LDE225 and chemotherapy to treat PDAC patients.

METHODS

This was a multi-center, phase I/II study for patients with metastatic PDAC establishing the maximum tolerated dose of LDE225 co-administered with gemcitabine and nab-paclitaxel (phase I) and evaluating the efficacy and safety of the treatment combination after prior FOLFIRINOX treatment (phase II). Tumor microenvironment assessment was performed with quantitative MRI using intra-voxel incoherent motion diffusion weighted MRI (IVIM-DWI) and dynamic contrast-enhanced (DCE) MRI.

RESULTS

The MTD of LDE225 was 200 mg once daily co-administered with gemcitabine 1000 mg/m2 and nab-paclitaxel 125 mg/m2. In phase II, six therapy-related grade 4 adverse events (AE) and three grade 5 were observed. In 24 patients, the target lesion response was evaluable. Three patients had partial response (13%), 14 patients showed stable disease (58%), and 7 patients had progressive disease (29%). Median overall survival (OS) was 6 months (IQR 3.9-8.1). Blood plasma fraction (DCE) and diffusion coefficient (IVIM-DWI) significantly increased during treatment. Baseline perfusion fraction could predict OS (>222 days) with 80% sensitivity and 85% specificity.

CONCLUSION

LDE225 in combination with gemcitabine and nab-paclitaxel was well-tolerated in patients with metastatic PDAC and has promising efficacy after prior treatment with FOLFIRINOX. Quantitative MRI suggested that LDE225 causes increased tumor diffusion and works particularly well in patients with poor baseline tumor perfusion.

Multimodal functional imaging for early response assessment in patients with gastrointestinal stromal tumor treated with tyrosine kinase inhibitors

BACKGROUND

Several imaging modalities are used in the early work-up of patients with gastrointestinal stromal tumor (GIST) receiving tyrosine kinase inhibitor (TKI) treatment and there is a need to establish whether they provide similar or complimentary information.

PURPOSE

To compare 18F-fluorodeoxyglucose positron emission tomography (FDG PET), computed tomography (CT) and magnetic resonance imaging (MRI) as early predictors of three-month outcomes for patients with GIST receiving TKI treatment.

MATERIAL AND METHODS

Thirty-five patients with advanced GIST were prospectively included between February 2011 and June 2017. FDG PET, contrast-enhanced CT (CECT), and MRI were performed before and early after onset of TKI treatment (range 8-18 days). Early response was categorized according to mRECIST (CT), the Choi criteria (CECT), and PERCIST (FDG PET/CT). For MRI, volumetry from T2-weighted images and change in apparent diffusion coefficient (ADC) from diffusion-weighted imaging was used. The reference standard for early assessment was the three-month mRECIST evaluation based on CT. At three months, both stable disease (SD) and partial response (PR) were categorized as response. Clinical usefulness was defined as agreement between early and three-month assessment.

RESULTS

At the three-month assessment, 91% (32/35) were responders, 37% (13/35) PR, 54% (19/35) SD, and 9% (3/35) had progressive disease (PD). Early assessment correctly predicted three-month response in 93% (27/29) for MRI, 80% (28/35) for PERCIST, 74% (26/35) for Choi, and 23% (8/35) for mRECIST. Six patients had non-FDG-avid tumors. For the FDG-avid tumors, PET/CT correctly predicted three-month response in 97% (28/29).

CONCLUSION

MRI was superior to CECT for early assessment of TKI-treatment response in GIST. If the tumor was FDG-avid, PET and MRI were equally good. Changes in functional parameters were superior to changes in longest tumor diameter (mRECIST).

Multimodal Imaging Techniques to Evaluate the Anticancer Effect of Cold Atmospheric Pressure Plasma

BACKGROUND

Skin cancer is the most frequent cancer worldwide and is divided into non-melanoma skin cancer, including basal cell carcinoma, as well as squamous cell carcinoma (SCC) and malignant melanoma (MM).

METHODS

This study evaluates the effects of cold atmospheric pressure plasma (CAP) on SCC and MM in vivo, employing a comprehensive approach using multimodal imaging techniques. Longitudinal MR and PET/CT imaging were performed to determine the anatomic and metabolic tumour volume over three-weeks in vivo. Additionally, the formation of reactive species after CAP treatment was assessed by non-invasive chemiluminescence imaging of L-012. Histological analysis and immunohistochemical staining for Ki-67, ApopTag®, F4/80, CAE, and CD31, as well as protein expression of PCNA, caspase-3 and cleaved-caspase-3, were performed to study proliferation, apoptosis, inflammation, and angiogenesis in CAP-treated tumours.

RESULTS

As the main result, multimodal in vivo imaging revealed a substantial reduction in tumour growth and an increase in reactive species after CAP treatment, in comparison to untreated tumours. In contrast, neither the markers for apoptosis, nor the metabolic activity of both tumour entities was affected by CAP.

CONCLUSIONS

These findings propose CAP as a potential adjuvant therapy option to established standard therapies of skin cancer.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Semi-Automated Segmentation of Bone Metastases from Whole-Body MRI: Reproducibility of Apparent Diffusion Coefficient Measurements

Using semi-automated software simplifies quantitative analysis of the visible burden of disease on whole-body MRI diffusion-weighted images. To establish the intra- and inter-observer reproducibility of apparent diffusion coefficient (ADC) measures, we retrospectively analyzed data from 20 patients with bone metastases from breast (BCa; n = 10; aged 62.3 ± 14.8) or prostate cancer (PCa; n = 10; aged 67.4 ± 9.0) who had undergone examinations at two timepoints, before and after hormone-therapy. Four independent observers processed all images twice, first segmenting the entire skeleton on diffusion-weighted images, and then isolating bone metastases via ADC histogram thresholding (ADC: 650–1400 µm²/s). Dice Similarity, Bland-Altman method, and Intraclass Correlation Coefficient were used to assess reproducibility. Inter-observer Dice similarity was moderate (0.71) for women with BCa and poor (0.40) for men with PCa. Nonetheless, the limits of agreement of the mean ADC were just ±6% for women with BCa and ±10% for men with PCa (mean ADCs: 941 and 999 µm²/s, respectively). Inter-observer Intraclass Correlation Coefficients of the ADC histogram parameters were consistently greater in women with BCa than in men with PCa. While scope remains for improving consistency of the volume segmented, the observer-dependent variability measured in this study was appropriate to distinguish the clinically meaningful changes of ADC observed in patients responding to therapy, as changes of at least 25% are of interest.

In vivo assessment of Lauren classification for gastric adenocarcinoma using diffusion MRI with a fractional order calculus model

OBJECTIVES

To evaluate the performance of a fractional order calculus (FROC) diffusion model for imaging-based assessment of Lauren classification in gastric adenocarcinoma.

METHODS

In this study, 43 patients (15 females, 28 males) with gastric adenocarcinoma underwent MRI at 1.5 T. According to pathology-based Lauren classification, 10 patients had diffuse-type, 20 had intestinal-type, and 13 had mixed-type lesions. The diffuse and mixed types were combined as diffuse-and-mixed type to be differentiated from the intestinal type using diffusion MRI. Diffusion-weighted images were acquired by using eleven b-values (0-2000 s/mm²). Three FROC model parameters comprising diffusion coefficient D, intravoxel diffusion heterogeneity β, and a microstructural quantity μ, together with a conventional apparent diffusion coefficient (ADC), were estimated. The mean parameter values in the tumour were computed by using a percentile histogram analysis. Individual or linear combinations of the mean parameters in the tumour were used to differentiate the diffuse-and-mixed type from the intestinal type using descriptive statistics and receiver operating characteristic (ROC) analyses.

RESULTS

Significant differences were observed between diffuse-and-mixed-type and intestinal-type lesions in D (0.99 ± 0.20 μm²/ms vs. 1.11 ± 0.23 μm²/ms; p = 0.036), β (0.37 ± 0.08 vs. 0.43 ± 0.11; p = 0.043), μ (7.92 ± 2.79 μm vs. 9.87 ± 1.52 μm; p = 0.038), and ADC (0.81 ± 0.34 μm²/ms vs. 0.96 ± 0.19 μm²/ms; p = 0.033). Among the individual parameters, μ produced the largest area under the ROC curve (0.739). The combinations of (D, β, μ) and (β and μ) produced the best overall performance with a sensitivity of 0.739, specificity of 0.750, accuracy of 0.744, and area under the curve of 0.793 (95% confidence interval: 0.657-0.929).

CONCLUSION

Diffusion MRI with the FROC model holds promise for non-invasive assessment of Lauren classification for gastric adenocarcinoma.

KEY POINTS

• High b-value diffusion MRI with a FROC model that is sensitive to tissue microstructures can differentiate the diffuse-and-mixed type from intestinal type of gastric adenocarcinoma. • The combination of FROC parameters produced the best result for distinguishing the diffuse-and-mixed type from the intestinal type with an area under the receiver operating characteristic curve of 0.793. • The FROC model parameters, individually or conjointly, hold promise for repeated, non-invasive evaluations of gastric adenocarcinoma at various time points throughout disease progression or regression to complement conventional Lauren classification.

Cerebral microcirculation in glioblastoma: A major determinant of diagnosis, resection, and drug delivery

Glioblastoma (GBM) is the most common primary brain tumor with a dismal prognosis. Current standard of treatment is safe maximal tumor resection followed by chemotherapy and radiation. Altered cerebral microcirculation and elevated blood-tumor barrier (BTB) permeability in tumor periphery due to glioma-induced vascular dysregulation allow T1 contrast-enhanced visualization of resectable tumor boundaries. Newer tracers that label the tumor and its vasculature are being increasingly used for intraoperative delineation of glioma boundaries for even more precise resection. Fluorescent 5-aminolevulinic acid (5-ALA) and indocyanine green (ICG) are examples of such intraoperative tracers. Recently, magnetic resonance imaging (MRI)-based MR thermometry is being employed for laser interstitial thermal therapy (LITT) for glioma debulking. However, aggressive, fatal recurrence always occurs. Postsurgical chemotherapy is hampered by the inability of most drugs to cross the blood-brain barrier (BBB). Understanding postsurgical changes in brain microcirculation and permeability is crucial to improve chemotherapy delivery. It is important to understand whether any microcirculatory indices can differentiate between true recurrence and radiation necrosis. LITT leads to peri-ablation BBB opening that persists for several weeks. Whether it can be a conduit for chemotherapy delivery is yet to be explored. This review will address the role of cerebral microcirculation in such emerging ideas in GBM diagnosis and therapy.

Mean apparent diffusion coefficient in a single slice may predict tumor response to whole-brain radiation therapy in non-small-cell lung cancer patients with brain metastases

OBJECTIVES

This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC).

METHODS

A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started. Mean single-plane ADC value of ROIs was evaluated by two radiologists blinded to results of each other. The treatment response rate, intracranial progression-free survival (PFS), and overall survival (OS) were analyzed based on the ADC value and ΔADC respectively. At last, we used COX and logistic regression to do the multivariate analysis.

RESULTS

There was good inter-observer agreement of mean ADC value pre-WBRT, post-WBRT, and ΔADC between the 2 radiologists (Pearson correlation 0.915 [pre-WBRT], 0.950 [post-WBRT], 0.937 [ΔADC], p < 0.001, for each one). High mean ADC value were related with better response rate (72.2% vs 37.5%, p = 0.001) and iPFS (7.6 vs 6.4 months, p = 0.031). High ΔADC were related with better response rate (73.6% vs 36.7%, p < 0.001). Multivariate analysis shows that histopathology, BMs number, high ADC value pre-WBRT, and high ΔADC post-WBRT were related to better treatment response of WBRT, and KPS, BMs number, and low ADC value pre-WBRT increased the risk of developing intracranial relapse.

CONCLUSIONS

The mean single-plane ADC value pre-WBRT and ΔADC post-WBRT were potential predictor for intracranial tumor response to WBRT in NSCLC patients with brain metastases.

KEY POINTS

• ADC value is a potential predictor of intracranial treatment response to WBRT in NSCLC patients with brain metastases. • Higher mean ADC value pre-WBRT and ΔADC post-WBRT of brain metastases were related to better intracranial tumor response. • Prediction of response before WBRT using ADC value can help oncologists to make better therapy plans and avoid missing opportunities for rescue therapy.

Recent Advances in Nanomedicine for the Diagnosis and Treatment of Prostate Cancer Bone Metastasis

Patients with advanced prostate cancer can develop painful and debilitating bone metastases. Currently available interventions for prostate cancer bone metastases, including chemotherapy, bisphosphonates, and radiopharmaceuticals, are only palliative. They can relieve pain, reduce complications (e.g., bone fractures), and improve quality of life, but they do not significantly improve survival times. Therefore, additional strategies to enhance the diagnosis and treatment of prostate cancer bone metastases are needed. Nanotechnology is a versatile platform that has been used to increase the specificity and therapeutic efficacy of various treatments for prostate cancer bone metastases. In this review, we summarize preclinical research that utilizes nanotechnology to develop novel diagnostic imaging tools, translational models, and therapies to combat prostate cancer bone metastases.

Imaging for Response Assessment in Cancer Clinical Trials

The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.

What's New for Clinical Whole-body MRI (WB-MRI) in the 21st Century

Whole-body MRI (WB-MRI) has evolved since its first introduction in the 1970s as an imaging technique to detect and survey disease across multiple sites and organ systems in the body. The development of diffusion-weighted MRI (DWI) has added a new dimension to the implementation of WB-MRI on modern scanners, offering excellent lesion-to-background contrast, while achieving acceptable spatial resolution to detect focal lesions 5 to 10 mm in size. MRI hardware and software advances have reduced acquisition times, with studies taking 40-50 min to complete.The rising awareness of medical radiation exposure coupled with the advantages of MRI has resulted in increased utilization of WB-MRI in oncology, paediatrics, rheumatological and musculoskeletal conditions and more recently in population screening. There is recognition that WB-MRI can be used to track disease evolution and monitor response heterogeneity in patients with cancer. There are also opportunities to combine WB-MRI with molecular imaging on PET-MRI systems to harness the strengths of hybrid imaging. The advent of artificial intelligence and machine learning will shorten image acquisition times and image analyses, making the technique more competitive against other imaging technologies.

The apparent diffusion coefficient is a useful biomarker in predicting treatment response in patients with locally advanced rectal cancer

BACKGROUND

Apparent diffusion coefficient (ADC) values achieve promising results in treatment response prediction in patients with several types of cancers.

PURPOSE

To determine whether ADC values predict neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

Forty-four patients with LARC who underwent magnetic resonance imaging scans before and after nCRT followed by delayed surgery were enrolled retrospectively. The sample was distributed as follows: responders (R), n = 8; and non-responders (Non-R), n = 36. Three markers of treatment response were considered: post-nCRT measures; ΔADC; and Δ%ADC. Statistical analysis included a Wilcoxon test, a Mann-Whitney U test, and a receiver operating characteristic (ROC) analysis in order to evaluate the diagnostic accuracy for each ADC value marker to differentiate between R and Non-R.

RESULTS

Both minimum and mean ADC values were significantly higher after nCRT in the R group, while non-significant differences between basal and control ADC values were found in the non-R group. In addition, ΔADC and Δ%ADC exhibited increased values after nCRT in R when compared with non-R. ROC analysis revealed the following diagnostic performance parameters: post-nCRT: ADCmin = 1.05 × 10-3 mm2/s (sensitivity 61.1% and specificity 66.7%), ADCmean = 1.50 × 10-3 mm2/s (sensitivity 72.2% and specificity 83.3%), ΔADC: ADCmin = 0.35 (sensitivity 66.7% and specificity 83.3%), ADCmean = 0.50 (sensitivity 72% and specificity 83%); and Δ%ADC: ADCmin = 44% (sensitivity 66.7% and specificity 83.3%) and ADCmean = 60% (sensitivity 83% and specificity 99%).

CONCLUSION

Our findings suggest that post-treatment rectal tumor ADC values, as well changes between pre- and post-treatment values, may be biomarkers for predicting treatment response in patients with LARC who underwent nCRT.

Simultaneous multislice diffusion-weighted imaging with short tau inversion recovery fat suppression in bone-metastasizing breast cancer

PURPOSE

To compare image quality and ADC values of simultaneous multislice diffusion-weighted imaging (mb-DWI) with that of conventional DWI (c-DWI) using short tau inversion recovery fat saturation (STIR) in women with bone-metastasizing breast cancer.

METHOD

c-DWI and mb-DWI were acquired at 1.5 T in 23 breast cancer patients from skull base to mid thighs. mb-DWI was compared to c-DWI in terms of subjective image quality, artefacts and bone metastasis lesion conspicuity assessed on a 5-point Likert scale. ADC values of different organs as well as bone metastasis ADC values were compared between c-DWI and mb-DWI.

RESULTS

mb-DWI reduced scan time by 48 % compared with c-DWI (1 min 58 s vs. 3 min 45 s per station). mb-DWI provided similar subjective image quality (3.8 vs. 3.7, p = 0.70), number of artefacts (50 vs. 56), severity of these (4.6 vs. 4.7, p = 0.11), and lesion conspicuity (4.2 vs. 4.4, p = 0.31) compared to c-DWI. mb-DWI showed lower mean ADC values in liver (0.5 × 10^-3 mm²/s vs. 0.7 × 10^-3 mm²/s, p = 0.002) and erector spine muscle (1.3 × 10^-3 mm²/s vs. 1.5 × 10^-3 mm²/s, p < 0.001). Bone metastasis ADC values from mb-DWI were 6.4 % lower than c-DWI (95 % confidence interval: 5.4%-7.4%, p < 0.001).

CONCLUSIONS

mb-DWI provides similar subjective image quality to c-DWI with the same level of artefacts. Although bone metastasis ADC values were lower, mb-DWI can substantially reduce scan times of whole-body DWI in women with bone-metastasizing breast cancer.

Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response

Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

Tracking tumor radiotherapy response in vivo with Cherenkov-excited luminescence ink imaging

This study demonstrates remote imaging for in vivo detection of radiation-induced tumor microstructural changes by tracking the diffusive spread of injected intratumor UV excited tattoo ink using Cherenkov-excited luminescence imaging (CELI). Micro-liter quantities of luminescent tattoo ink with UV absorption and visible emission were injected at a depth of 2 mm into mouse tumors prior to receiving a high dose treatment of radiation. X-rays from a clinical linear accelerator were used to excite phosphorescent compounds within the tattoo ink through Cherenkov emission. The in vivo phosphorescence was detected using a time-gated intensified CMOS camera immediately after injection, and then again at varying time points after the ink had broken down with the apoptotic tumor cells. Ex vivo tumors were imaged post-mortem using hyperspectral cryo-fluorescence imaging to quantify necrosis and compared to Cherenkov-excited light imaging of diffusive ink spread measured in vivo. Imaging of untreated control mice showed that ink distributions remained constant after four days with less than 3% diffusive spread measured using full width at 20% max. For all mice, in vivo CELI measurements matched within 12% of the values estimated by the high-resolution ex vivo sliced luminescence imaging of the tumors. The tattoo ink spread in treated mice was found to correlate well with the nonperfusion necrotic core volume (R2 = 0.92) but not well with total tumor volume changes (R2 = 0.34). In vivo and ex vivo findings indicate that the diffusive spread of the injected tattoo ink can be related to radiation-induced necrosis, independent of total tumor volume change. Tracking the diffusive spread of the ink allows for distinguishing between an increase in tumor size due to new cellular growth and an increase in tumor size due to edema. Furthermore, the imaging resolution of CELI allows for in vivo tracking of subtle microenvironmental changes which occur earlier than tumor shrinkage and this offers the potential for novel, minimally invasive radiotherapy response assay without interrupting a singular clinical workflow.

Optimal timing for prediction of pathologic complete response to neoadjuvant chemoradiotherapy with diffusion-weighted MRI in patients with esophageal cancer

OBJECTIVE

This study was conducted in order to determine the optimal timing of diffusion-weighted magnetic resonance imaging (DW-MRI) for prediction of pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) for esophageal cancer.

METHODS

Patients with esophageal adenocarcinoma or squamous cell carcinoma who planned to undergo nCRT followed by surgery were enrolled in this prospective study. Patients underwent six DW-MRI scans: one baseline scan before the start of nCRT and weekly scans during 5 weeks of nCRT. Relative changes in mean apparent diffusion coefficient (ADC) values between the baseline scans and the scans during nCRT (ΔADC(%)) were compared between pathologic complete responders (pCR) and non-pCR (tumor regression grades 2-5). The discriminative ability of ΔADC(%) was determined based on the c-statistic.

RESULTS

A total of 24 patients with 142 DW-MRI scans were included. pCR was observed in seven patients (29%). ΔADC(%) from baseline to week 2 was significantly higher in patients with pCR versus non-pCR (median [IQR], 36% [30%, 41%] for pCR versus 16% [14%, 29%] for non-pCR, p = 0.004). The ΔADC(%) of the second week in combination with histology resulted in the highest c-statistic for the prediction of pCR versus non-pCR (0.87). The c-statistic of this model increased to 0.97 after additional exclusion of patients with a small tumor volume (< 7 mL, n = 3) and tumor histology of the resection specimen other than adenocarcinoma or squamous cell carcinoma (n = 1).

CONCLUSION

The relative change in tumor ADC (ΔADC(%)) during the first 2 weeks of nCRT is the most predictive for pathologic complete response to nCRT in esophageal cancer patients.

KEY POINTS

• DW-MRI during the second week of neoadjuvant chemoradiotherapy is most predictive for pathologic complete response in esophageal cancer. • A model including ΔADCweek 2was able to discriminate between pathologic complete responders and non-pathologic complete responders in 87%. • Improvements in future MRI studies for esophageal cancer may be obtained by incorporating motion management techniques.

Double Diffusion Encoding for Probing Radiation-Induced Microstructural Changes in a Tumor Model: A Proof-of-Concept Study With Comparison to the Apparent Diffusion Coefficient and Histology

BACKGROUND

Microstructure analyses are gaining interest in cancer MRI as an alternative to the conventional apparent diffusion coefficient (ADC), of which the determinants remain unclear.

PURPOSE

To assess the sensitivity of parameters calculated from a double diffusion encoding (DDE) sequence to changes in a tumor's microstructure early after radiotherapy and to compare them with ADC and histology.

STUDY TYPE

Cohort study on experimental tumors.

ANIMAL MODEL

Sixteen WAG/Rij rats grafted with one rhabdomyosarcoma fragment in each thigh. Thirty-one were imaged at days 1 and 4, of which 17 tumors received a 20 Gy radiation dose after the first imagery.

FIELD STRENGTH/SEQUENCE

3T. Diffusion-weighted imaging, DDE with flow compensated, and noncompensated measurements.

ASSESSMENTS

1) To compare, after irradiation, DDE-derived parameters (intracellular fraction, cell size, and cell density) to their histological counterparts (fraction of stained area, minimal Feret diameter, and nuclei count, respectively). 2) To compare percentage changes in DDE-derived parameters and ADC. 3) To evaluate the evolution of DDE-derived parameters describing perfusion.

STATISTICAL TESTS

Wilcoxon rank sum test.

RESULTS

1) Intracellular fraction, cell size, and cell density were respectively lower (-24%, P < 0.001), higher (+7.5%, P < 0.001) and lower (-38%, P < 0.001) in treated tumors as compared to controls. Fraction of stained area, minimal Feret diameter, and nuclei count were respectively lower (-20%, P < 0.001), higher (+28%, P < 0.001), and lower (-34%, P < 0.001) in treated tumors. 2) The magnitude of ADC's percentage change due to irradiation (16.4%) was superior to the one of cell size (8.4%, P < 0.01) but inferior to those of intracellular fraction (35.5%, P < 0.001) and cell density (42%, P < 0.001). 3) After treatment, the magnitude of the vascular fraction's decrease was higher than the increase of flow velocity (33.3%, vs. 13.3%, P < 0.001).

DATA CONCLUSION

The DDE sequence allows quantitatively monitoring the effects of radiotherapy on a tumor's microstructure, whereas ADC only reveals global changes.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 4. J. Magn. Reson. Imaging 2020;52:941-951.

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

Background

Diffusion weighted magnetic resonance imaging (DW-MRI) holds great potential for monitoring treatment response in cancer patients shortly after initiation of radiotherapy. It is hypothesized that a decrease in cellular density of irradiated cancerous tissue will lead to an increase in quantitative apparent diffusion coefficient (ADC) values. DW-MRI can therefore serve as a non-invasive marker of cell death and apoptosis in response to treatment. In the present study, we aimed to investigate the applicability of DW-MRI in preclinical models to monitor radiation-induced treatment response. In addition, we compared DW-MRI with ex vivo measures of cell density, cell death and apoptosis.

Methods

DW-MRI was tested in two different syngeneic mouse models, a colorectal cancer (CT26) and a breast cancer (4 T1). ADC values were compared with quantitative determinations of apoptosis and cell death by flow cytometry. Furthermore, ADC-values were also compared to histological measurement of cell density on tumor sections.

Results

We found a significant correlation between ADC-values and apoptotic state in the CT26 model (P = 0.0031). A strong correlation between the two measurements of ADC-value and apoptotic state was found in both models, which were also present when comparing ADC-values to cell densities.

Conclusions

Our findings demonstrate that DW-MRI can be used for non-invasive monitoring of radiation-induced changes in cell state during cancer therapy. ADC values reflect ex vivo cell density and correlates well with apoptotic state, and can hereby be described as a marker for the cell state after therapy and used as a non-invasive response marker.

Stimulated-echo diffusion-weighted imaging with moderate b values for the detection of prostate cancer

OBJECTIVES

Conventional spin-echo (SE) DWI leads to a fundamental trade-off depending on the b value: high b value provides better lesion contrast-to-noise ratio (CNR) by sacrificing signal-to-noise ratio (SNR), image quality, and quantitative reliability. A stimulated-echo (STE) DWI acquisition is evaluated for high-CNR imaging of prostate cancer while maintaining SNR and reliable apparent diffusion coefficient (ADC) mapping.

METHODS

In this prospective, IRB-approved study, 27 patients with suspected prostate cancer (PCa) were scanned with three DWI sequences (SE b = 800 s/mm², SE b = 1500 s/mm², and STE b = 800 s/mm²) after informed consent was obtained. ROIs were drawn on biopsy-confirmed cancer and non-cancerous tissue to perform quantitative SNR, CNR, and ADC measurements. Qualitative metrics (SNR, CNR, and overall image quality) were evaluated by three experienced radiologists. Metrics were compared pairwise between the three acquisitions using a t test (quantitative metrics) and Wilcoxon rank test (qualitative metrics).

RESULTS

Quantitative measurements showed that STE DWI at b = 800 s/mm² has significantly better SNR compared to SE DWI at b = 1500 s/mm² (p < 0.0001) and comparable CNR to high-b value SE DWI at b = 1500 s/mm² (p < 0.05) in the peripheral zone. Qualitative assessment showed preference to STE b = 800 s/mm² in SNR and SE b = 1500 s/mm² in CNR. The overall image quality and lesion detectability among most readers showed no significant preference between STE b = 800 s/mm² and SE b = 1500 s/mm². Further, STE DWI had similar ADC contrast between lesion and normal tissue as SE DWI at b = 800 s/mm² (p = 0.90).

CONCLUSION

STE DWI has the potential to provide high-SNR, high-CNR imaging of prostate cancer while also enabling reliable ADC mapping.

KEY POINTS

• Quantitative analysis showed that STE DWI at b = 800 s/mm²is able to achieve simultaneously high CNR, high SNR, and reliable ADC mapping, compared to SE b = 800 s/mm²and SE b = 1500 s/mm². • Qualitative assessment by three readers showed that STE DWI at b = 800 s/mm²has significantly higher SNR than SE b = 1500 s/mm². No preference between SE b = 1500 s/mm²and STE b = 800 s/mm²was determined in terms of CNR with no missed lesions were found in both acquisitions. • A single STE DWI acquisition at moderate b value (800-1000 s/mm²) may provide sufficient image quality and quantitative reliability for prostate cancer imaging within a shorter scan time, in place of two DWI acquisitions (one with moderate b value and one with high b value).

Cell Tracking in Cancer Immunotherapy

The impressive development of cancer immunotherapy in the last few years originates from a more precise understanding of control mechanisms in the immune system leading to the discovery of new targets and new therapeutic tools. Since different stages of disease progression elicit different local and systemic inflammatory responses, the ability to longitudinally interrogate the migration and expansion of immune cells throughout the whole body will greatly facilitate disease characterization and guide selection of appropriate treatment regiments. While using radiolabeled white blood cells to detect inflammatory lesions has been a classical nuclear medicine technique for years, new non-invasive methods for monitoring the distribution and migration of biologically active cells in living organisms have emerged. They are designed to improve detection sensitivity and allow for a better preservation of cell activity and integrity. These methods include the monitoring of therapeutic cells but also of all cells related to a specific disease or therapeutic approach. Labeling of therapeutic cells for imaging may be performed in vitro, with some limitations on sensitivity and duration of observation. Alternatively, in vivo cell tracking may be performed by genetically engineering cells or mice so that may be revealed through imaging. In addition, SPECT or PET imaging based on monoclonal antibodies has been used to detect tumors in the human body for years. They may be used to detect and quantify the presence of specific cells within cancer lesions. These methods have been the object of several recent reviews that have concentrated on technical aspects, stressing the differences between direct and indirect labeling. They are briefly described here by distinguishing ex vivo (labeling cells with paramagnetic, radioactive, or fluorescent tracers) and in vivo (in vivo capture of injected radioactive, fluorescent or luminescent tracers, or by using labeled antibodies, ligands, or pre-targeted clickable substrates) imaging methods. This review focuses on cell tracking in specific therapeutic applications, namely cell therapy, and particularly CAR (Chimeric Antigen Receptor) T-cell therapy, which is a fast-growing research field with various therapeutic indications. The potential impact of imaging on the progress of these new therapeutic modalities is discussed.

Developing a Pipeline for Multiparametric MRI-Guided Radiation Therapy: Initial Results from a Phase II Clinical Trial in Newly Diagnosed Glioblastoma

Quantitative mapping of hyperperfused and hypercellular regions of glioblastoma has been proposed to improve definition of tumor regions at risk for local recurrence following conventional radiation therapy. As the processing of the multiparametric dynamic contrast-enhanced (DCE-) and diffusion-weighted (DW-) magnetic resonance imaging (MRI) data for delineation of these subvolumes requires additional steps that go beyond the standard practices of target definition, we sought to devise a workflow to support the timely planning and treatment of patients. A phase II study implementing a multiparametric imaging biomarker for tumor hyperperfusion and hypercellularity consisting of DCE-MRI and high b-value DW-MRI to guide intensified (75 Gy/30 fractions) radiation therapy (RT) in patients with newly diagnosed glioblastoma was launched. In this report, the workflow and the initial imaging outcomes of the first 12 patients are described. Among all the first 12 patients, treatment was initiated within 6 weeks of surgery and within 2 weeks of simulation. On average, the combined hypercellular volume and high cerebral blood volume/tumor perfusion volume were 1.8 times smaller than the T1 gadolinium abnormality and 10 times smaller than the FLAIR abnormality. Hypercellular volume and high cerebral blood volume/tumor perfusion volume each identified largely distinct regions and showed 57% overlap with the enhancing abnormality, and minimal-to-no extension outside of the FLAIR. These results show the feasibility of implementing a workflow for multiparametric magnetic resonance-guided radiation therapy into clinical trials with a coordinated multidisciplinary team, and the unique and complementary tumor subregions identified by the combination of high b-value DW-MRI and DCE-MRI.

Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI

Background

The values that have been received from apparent diffusion coefficient (ADC) maps of diffusion-weighted magnetic resonance imaging (DW-MRI) might play a vital role in evaluating tumors and their grading scale.

Purpose

To investigate the predictive role of this heterogeneity in brain tumor pathologies and its correlation with Ki-67.

Material and Methods

A total of 124 patients with brain tumors underwent brain MRI with gadolinium injection. ADC and standard deviation of each lesion have been obtained from manual localization of the region of interest on the ADC map. A receiver operating characteristic analysis was conducted to determine the minimum cut-off values of the mean ADC and mean standard deviation of ADC maps having the highest sensitivity and specificity to differentiate high-grade and low-grade tumors.

Results

Mean ADC values in the region of interest were significantly lower for malignant tumors (grade IV and metastasis) than grade I brain tumors, while a higher mean standard deviation was observed. In a more detailed comparison of tumor groups, the mean standard deviation of the ADC for glioblastoma multiform was significantly higher than meningioma grade I ( P < 0.001) and metastasis was significantly higher than grade III and IV astrocytic tumors ( P = 0.004). The analysis of Ki-67 proliferation index and mean ADC values in gliomas showed a significant inverse correlation between the parameters (r = –0.0429, P < 0.001) and direct correlation between Ki-67 and mean standard deviation of the ADC (r = 0.551, P < 0.001). As an index for the ADC to differentiate high-grade and low-grade tumors, the cut-off values of 1.40*10−3 mm2/s for mean ADC and 45*10−3 mm2/s for mean standard deviation have the highest combination of sensitivity, specificity, and area under the curve.

Conclusion

The mean value and standard deviation of the ADC could be considered for differentiating between low-grade and high-grade brain tumors, as two available non-invasive methods.

Imaging of Preclinical Endometrial Cancer Models for Monitoring Tumor Progression and Response to Targeted Therapy

Endometrial cancer is the most common gynecologic malignancy in industrialized countries. Most patients are cured by surgery; however, about 15% of the patients develop recurrence with limited treatment options. Patient-derived tumor xenograft (PDX) mouse models represent useful tools for preclinical evaluation of new therapies and biomarker identification. Preclinical imaging by magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), single-photon emission computed tomography (SPECT) and optical imaging during disease progression enables visualization and quantification of functional tumor characteristics, which may serve as imaging biomarkers guiding targeted therapies. A critical question, however, is whether the in vivo model systems mimic the disease setting in patients to such an extent that the imaging biomarkers may be translatable to the clinic. The primary objective of this review is to give an overview of current and novel preclinical imaging methods relevant for endometrial cancer animal models. Furthermore, we highlight how these advanced imaging methods depict pathogenic mechanisms important for tumor progression that represent potential targets for treatment in endometrial cancer.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

Diffusion gradient nonlinearity bias correction reduces bias of breast cancer bone metastasis ADC values

CONTRACT GRANT SPONSOR

Health Research Fund of Central Denmark Region.

BACKGROUND

Diffusion gradient nonlinearity (DGNL) bias causes apparent diffusion coefficient (ADC) values to drop with increasing superior-inferior (SI) isocenter offset. This is a concern when performing quantitative diffusion-weighted imaging (DWI).

PURPOSE/HYPOTHESIS

To investigate if DGNL ADC bias can be corrected in breast cancer bone metastases using a clinical DWI protocol and an online correction algorithm.

STUDY TYPE

Prospective.

SUBJECTS/PHANTOM

A diffusion phantom (Model 128, High Precision Devices, Boulder, CO) was used for in vitro validation. Twenty-three women with bone-metastasizing breast cancer were enrolled to assess DGNL correction in vivo.

FIELD STRENGTH/SEQUENCE

DWI was performed on a 1.5T MRI system as single-shot, spin-echo, echo-planar imaging with short-tau inversion recovery (STIR) fat-saturation. ADC maps with and without DGNL correction were created from the b50 and b800 images.

ASSESSMENT

Uncorrected and DGNL-corrected ADC values were measured in phantom and bone metastases by placing regions of interest on b800 images and copying them to the ADC map. The SI offset was recorded.

STATISTICAL TESTS

In all, 79 bone metastases were assessed. ADC values with and without DGNL correction were compared at 14 cm SI offset using a two-tailed t-test.

RESULTS

In the diffusion phantom, DGNL correction increased SI offset, where ADC bias was lower than 5%, from 7.3-13.8 cm. Of the 23 patients examined, six had no metastases in the covered regions. In the remaining patients, bias of uncorrected bone metastasis ADC values was 19.1% (95% confidence interval [CI]: 15.4-22.9%) at 14 cm SI offset. After DGNL correction, ADC bias was significantly reduced to 3.5% (95% CI: 0.7-6.3%, P < 0.001), thus reducing bias due to DGNL by 82%.

DATA CONCLUSION

Online DGNL correction corrects DGNL ADC value bias and allows increased station lengths in the SI direction.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:904-911.

In vivo magnetic resonance imaging of treatment-induced apoptosis

Imaging apoptosis could provide an early and specific means to monitor tumor responses to treatment. To date, despite numerous attempts to develop molecular imaging approaches, there is still no widely-accepted and reliable method for in vivo imaging of apoptosis. We hypothesized that the distinct cellular morphologic changes associated with treatment-induced apoptosis, such as cell shrinkage, cytoplasm condensation, and DNA fragmentation, can be detected by temporal diffusion spectroscopy imaging (TDSI). Cetuximab-induced apoptosis was assessed in vitro and in vivo with cetuximab-sensitive (DiFi) and insensitive (HCT-116) human colorectal cancer cell lines by TDSI. TDSI findings were complemented by flow cytometry and immunohistochemistry. Cell cycle analysis and flow cytometry detected apoptotic cell shrinkage in cetuximab-treated DiFi cells, and significant apoptosis was confirmed by histology. TDSI-derived parameters quantified key morphological changes including cell size decreases during apoptosis in responsive tumors that occurred earlier than gross tumor volume regression. TDSI provides a unique measurement of apoptosis by identifying cellular characteristics, particularly cell shrinkage. The method will assist in understanding the underlying biology of solid tumors and predict tumor response to therapies. TDSI is free of any exogenous agent or radiation, and hence is very suitable to be incorporated into clinical applications.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.