Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis.
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ABSTRACT:
SUBMITTER: Lee MK
PROVIDER: S-EPMC8460673 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.
| S-EPMC9497087 | biostudies-literature
Project description:To compare region of interest (ROI)-apparent diffusion coefficient (ADC) on diffusion-weighted imaging (DWI) measurements and Ki-67 proliferation index before and after neoadjuvant chemotherapy (NACT) for breast cancer. 55 women were enrolled in this prospective single-center study, with a final population of 47 women (49 cases of invasive breast cancer). ROI-ADC measurements were obtained on MRI before and after NACT and were compared to histological findings, including the Ki-67 index in the whole study population and in subgroups of "pathologic complete response" (pCR) and non-pCR. Nineteen percent of women experienced pCR. There was a significant inverse correlation between Ki-67 index and ROI-ADC before NACT (r = - 0.443, p = 0.001) and after NACT (r = - 0.614, p < 0.001). The mean Ki-67 index decreased from 45.8% before NACT to 18.0% after NACT (p < 0.001), whereas the mean ROI-ADC increased from 0.883 × 10-3 mm2/s before NACT to 1.533 × 10-3 mm2/s after NACT (p < 0.001). The model for the prediction of Ki67 index variations included patient age, hormonal receptor status, human epidermal growth factor receptor 2 status, Scarff-Bloom-Richardson grade 2, and ROI-ADC variations (p = 0.006). After NACT, a significant increase in breast cancer ROI-ADC on diffusion-weighted imaging was observed and a significant decrease in the Ki-67 index was predicted. Clinical trial registration number: clinicaltrial.gov NCT02798484, date: 14/06/2016.
| S-EPMC9837175 | biostudies-literature
Project description:ObjectiveTo evaluate amide proton weighted (APTw) MRI combined with diffusion-weighted imaging (DWI) in predicting neoadjuvant chemoradiotherapy (NCRT) response in patients with locally advanced rectal cancer (LARC).Methods53 patients with LARC were enrolled in this retrospective study. MR examination including APTw MRI and DWI was performed before and after NCRT. APTw SI, ADC value, tumor size, CEA level before and after NCRT were assessed. The difference of the above parameters between before and after NCRT was calculated. The tumor regression grading (TRG) was assessed by American Joint Committee on Cancer's Cancer Staging Manual AJCC 8th score. The Shapiro-Wilk test, paired t-test and Wilcoxon Signed Ranks test, two-sample t-test, Mann-Whitney U test and multivariate analysis were used for statistical analysis.ResultsOf the 53 patients, 19 had good responses (TRG 0-1), 34 had poor responses (TRG 2-3). After NCRT, all the rectal tumors demonstrated decreased APT values, increased ADC values, reduced tumor volumes and CEA levels (all p < 0.001). Good responders demonstrated higher pre-APT values, higher Δ APT values, lower pre- ADC values and higher Δ tumor volumes than poor responders. Pre-APT combined with pre-ADC achieved the best diagnostic performance, with AUC of 0.895 (sensitivity of 85.29%, specificity of 89.47%, p < 0.001) in predicting good response to NCRT.ConclusionThe combination of APTw and DWI may serve as a noninvasive biomarker for evaluating and identifying response to NCRT in LARC patients.
| S-EPMC8281887 | biostudies-literature
Project description:BackgroundAccurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies to high-risk individuals. Our aim was to evaluate the value of baseline MRI-based radiomics machine-learning models in predicting the disease progression in nasopharyngeal carcinoma patients who achieved complete response after treatment.MethodsIn this retrospective study, 171 patients with pathologically confirmed nasopharyngeal carcinoma were included. Using hold-out cross validation scheme (7:3), relevant radiomic features were selected with the least absolute shrinkage and selection operator method based on baseline T2-weighted fat suppression and contrast-enhanced T1-weighted images in the training cohort. After Pearson's correlation analysis of selected radiomic features, multivariate logistic regression analysis was applied to radiomic features and clinical characteristics selection. Logistic regression analysis and support vector machine classifier were utilized to build the predictive model respectively. The predictive accuracy of the model was evaluated by ROC analysis along with sensitivity, specificity and AUC calculated in the validation cohort.ResultsA prediction model using logistic regression analysis comprising 4 radiomics features (HGLZE_T2H, HGLZE_T1, LDLGLE_T1, and GLNU_T1) and 5 clinical features (histology, T stage, N stage, smoking history, and age) showed the best performance with an AUC of 0.75 in the training cohort (95% CI: 0.66-0.83) and 0.77 in the validation cohort (95% CI: 0.64-0.90). The nine independent impact factors were entered into the nomogram. The calibration curves for probability of 3-year disease progression showed good agreement. The features of this prediction model showed satisfactory clinical utility with decision curve analysis.ConclusionsA radiomics model derived from pretreatment MR showed good performance for predicting disease progression in nasopharyngeal carcinoma and may help to improve clinical decision making.
| S-EPMC8800208 | biostudies-literature
Project description:PurposeThis study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation.MethodsOur study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk.ResultsA total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957-0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71-0.95) and 0.82 (95% CI: 0.68-0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88-0.93).ConclusionPrediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.
| S-EPMC9121398 | biostudies-literature
Project description:BackgroundFunctional MRI (fMRI) parameters analysis has been proven to be a promising tool of predicting therapeutic response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). The study was designed to identify and compare the value of fMRI parameters in predicting early response to IC in patients with NPC.MethodsThis prospective study enrolled fifty-six consecutively NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocols were performed before and after IC. Parameters maps (ADC, MD, MK, Dslow, Dfast, PF, Ktrans, Ve and Kep) of the primary tumor were calculated by the Functool post-processing software. The participants were classified as responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between RG with NRG. Logistic regression analysis and ROC were performed to further identify and compare the efficacy of the parameters.ResultsAfter IC, the mean values of ADC(p < 0.001), MD(p < 0.001), Dslow(p = 0.001), PF(p = 0.030) and Ve(p = 0.003) significantly increased, while MK(p < 0.001), Dfast(p = 0.009) and Kep(p = 0.003) values decreased dramatically, while no significant difference was detected in Ktrans(p = 0.130). Compared with NRG, ADC-pre(p < 0.001), MD-pre(p < 0.001) and Dslow-pre(p = 0.002) values in RG were lower, while MK-pre(p = 0.017) values were higher. The areas under the ROC curves for the ADC-pre, MD-pre, MK-pre, Dslow-pre and PRE were 0.885, 0.855, 0.809, 0.742 and 0.912, with the optimal cutoff value of 1210 × 10- 6 mm2/s, 1010 × 10- 6 mm2/s, 832 × 10- 6, 835 × 10- 6 mm2/s and 0.799 respectively.ConclusionsThe pretreatment conventional DWI (ADC), DKI (MD and MK), and IVIM (Dslow) values derived from fMRI showed a promising potential in predicting the response of the primary tumor to IC in NPC patients.Trial registrationThis study was approved by ethics board of the Chinese PLA General Hospital, and registered on January 30, 2021, in Chinese Clinical Trial Registry ( ChiCTR2100042863 ).
| S-EPMC8579637 | biostudies-literature
Project description:To determine the prognostic value of pre-treatment apparent diffusion coefficient (ADC) of colorectal liver metastases in predicting disease response, progression-free survival (PFS) and overall survival (OS).We retrospectively reviewed 102 patients who underwent pre-treatment diffusion-weighted MRI using a breath-hold (b=0, 150, 500) or a free-breathing (b=0, 50, 100, 250, 500, 750) technique. The mean ADC (b=0-500) and mean flow-insensitive ADC (ADChigh) values (breath-hold: b=150 and 500; free-breathing: b=100 and 500) of up to three hepatic lesions were evaluated in each patient. Clinical and laboratory parameters were recorded. Tumour response was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) criteria at 12 weeks after treatment. Associations between tumour response, ADC values and clinical/laboratory parameters were examined by one-way analysis of variance. The relationship of ADC with PFS and OS was determined by Kaplan-Meier analysis.62 patients responded to chemotherapy at 12 weeks. The pre-treatment mean ADC and mean ADChigh were higher in the non-responding group than in the responding group (1.55 vs 1.36, p=0.033; 1.40 vs 1.16, p=0.024). However, the PFS and OS of the two groups of patients stratified by the median of mean ADC values or threshold derived by receiver operating characteristic analysis were not statistically significant. By multivariate Cox regression analysis, patients with ≤2 metastases and response to chemotherapy showed better PFS; white cell count ≤10 and surgical treatment were associated with better OS.Colorectal liver metastasis with higher pre-treatment mean ADC and mean ADChigh was associated with poorer response to chemotherapy. However, ADC and ADChigh values did not predict the patient outcome in this study cohort.High mean ADC values of colorectal liver metastases on pre-treatment diffusion-weighted MRI is associated with poorer response to chemotherapy.
| S-EPMC3798332 | biostudies-other
Project description:The importance of Diffusion Weighted Imaging (DWI) in hepatocellular carcinoma (HCC) has been widely handled in the literature. Due to the mono-exponential model limitations, several studies recently investigated the role of non-Gaussian DWI models in HCC. However, their results are variable and inconsistent. Therefore, the aim of this systematic review is to summarize current knowledge on non-Gaussian DWI techniques in HCC. A systematic search of the literature, including PubMed, Google Scholar, MEDLINE, and ScienceDirect databases, was performed to identify original articles since 2010 that evaluated the role of non-Gaussian DWI models for HCC diagnosis, grading, response to treatment, and prognosis. Studies were grouped and summarized according to the non-Gaussian DWI models investigated. We focused on the most used non-Gaussian DWI models (Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Stretched Exponential-SE). The quality of included studies was evaluated by using QUADAS-2 and QUIPS tools. Forty-three articles were included, with IVIM and DKI being the most investigated models. Although the role of non-Gaussian DWI models in clinical settings has not fully been established, our findings showed that their parameters may potentially play a role in HCC. Further studies are required to identify a standardized DWI acquisition protocol for HCC diagnosis, grading, response to treatment, and prognosis.
| S-EPMC8232758 | biostudies-literature
Project description:The DREAM study will assess the diagnostic accuracy of diffusion-weighted MRI in combination with other imaging modalities (multiparametric MRI and CT Scan) in determining the true status of disappearing liver metastasis (DLM) detected after conversion systemic therapy for unresectable or borderline resectable colorectal liver metastasis (CRLM).
| 2215891 | ecrin-mdr-crc
Project description:PurposePatients with cancers of oropharynx have a favorable prognosis and are an ideal candidate for adaptive therapy. A replan to improve coverage or escalate/de-escalate dose based on morphological information alone may not be adequate as the grossly involved lymph nodes (LNs) of a subset of these patients tend to become cystic and often do not regress. Functional adaptation may be a better approach when considering replanning for these patients. The purpose of this study was to evaluate the weekly trends in treatment related morphological and physiological changes for these LNs using diffusion-weighted MRI (DW-MRI) and evaluate its implications for adaptive replanning.MethodsTen patients with histologically proven oropharynx HNSCC undergoing concurrent chemoradiation were analyzed in this study. MR imaging protocol included axial T1w, T2w, and DW-MRI using a 3 T Philips MR scanner. The patients were scanned weekly in radiation treatment planning position using a 16 element phased-array anterior coil and a 44 element posterior coil. A total of 65 DWI and T2w scans were analyzed. DWI was performed using an optimized single-shot echo planar imaging sequence (TR/TE = 5000/65 ms, slice thickness = 5 mm; slices = 28; b values = 0 and 800 s/mm(2)). Quantification of the DW-MRI images was performed by calculating the apparent diffusion coefficient (ADC). T2w and DWI scans were imported into the Eclipse treatment planning system and gross tumor volumes (GTVs) corresponding to grossly involved LNs were contoured on each axial slice by physician experts. An attempt was made to remove any cystic or necrotic components so that the ADC analysis was of viable tumor only. A pixel-by-pixel fit of signal intensities within the GTVs was performed assuming monoexponential behavior. From each GTV histogram mean, median, standard deviation, skewness, and kurtosis were calculated. Absolute and percent change in weekly ADC histogram parameters and percent change in T2w GTV were also calculated.ResultsFor all nodes, an immediate change in ADC was observed during first 2-3 weeks after which ADC values either continued to increase or plateaued. A few nodal volumes had a slightly decreased ADC value during later weeks. Percent increase in median ADC from weeks 1 to 6 with respect to baseline was 14%, 25%, 41%, 42%, 45%, and 58%. The corresponding change in median T2 volumes was 8%, 10%, 16%, 22%, 40%, and 42%, respectively. The ADC distribution of the viable tumors was initially highly kurtotic; however, the kurtosis decreased as treatment progressed. The ADC distribution also showed a higher degree of skewness in the first 2 weeks, progressively becoming less skewed as treatment progressed so as to slowly approach a more symmetric distribution.ConclusionsPhysiological changes in LNs represented by changes in ADC evaluated using DW-MRI are evident sooner than the morphological changes calculated from T2w MRI. The decisions for adaptive replanning may need to be individualized and should be based primarily on tumor functional information. The authors' data also suggest that for many patients, week 3 maybe the optimal time to intervene and replan. Larger studies are needed to confirm their findings.
| S-EPMC5360161 | biostudies-literature