Pretreatment prediction of brain tumors' response to radiation therapy using high b-value diffusion-weighted MRI

Clinical Value of a Nomogram Model Based on Apparent Diffusion Coefficient Values Within 1 cm of the Tumor Cavity to Predict Postoperative Progression of Glioma

OBJECTIVE

To explore the clinical value of constructing a nomogram model based on apparent diffusion coefficient values within 1 cm of the residual tumor cavity to predict the postoperative progression of gliomas.

METHODS

Clinical data of patients with glioma who underwent surgery were retrospectively retrieved from the First Hospital of Qinhuangdao. The mean apparent diffusion coefficient (mADC) was measured using a picture archiving and communication system. The Kaplan-Meier survival curve was constructed with the optimal mADC threshold determined by the X-tile. A nomogram was developed based on the independent risk factors determined using the Cox proportional hazards model (Cox regression model) to predict the progression of postoperative glioma. A receiver operating characteristic curve was drawn to evaluate the prediction accuracy of the model, and decision curve analysis was performed to assess the clinical value of the nomogram.

RESULTS

There was good agreement between the mADC values of the 2 repeated measurements before and after, with a consistency correlation coefficient of 0.83. Multivariate Cox regression analysis showed that peritumoral mADC values, degree of peritumoral enhancement, age, pathological grading, and degree of tumor resection were independent risk factors for predicting postoperative progression of glioma (all P < 0.05). The receiver operating characteristic curves of the nomogram predicting 1, 2, and 3 years postoperative progression were 0.86, 0.82, and 0.91, respectively. The calibration curve showed good consistency between the observed and predicted values in the model. The curve showed that the nomogram model has a good clinical application value.

CONCLUSIONS

The peritumoral mADC values, degree of peritumoral enhancement, age, pathological grade, and degree of tumor resection were independent factors affecting the postoperative progression of glioma. The nomogram model established for the first time based on mADC values within 1 cm of the tumor can predict the postoperative condition of patients with glioma intuitively and comprehensively. It can provide a relatively accurate prediction tool for neurosurgeons to individualize the evaluation of survival and prognosis, and formulate treatment plans for patients.

Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies

PURPOSE

Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies.

METHODS

For the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland-Altman analysis.

RESULTS

The in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10-3, 0.00 ± 0.06 × 10-3, and 1.13 ± 4.04 × 10-3 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as -0.01 ± 0.07.

CONCLUSION

Deep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR.

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

Preoperative and postoperative high angular resolution diffusion imaging tractography of cerebellar pathways in posterior fossa tumors

This study aimed to utilize high angular resolution diffusion magnetic resonance imaging (HARDI) tractography in the mapping of the pathways of the cerebellum associated with posterior fossa tumors (infratentorial neoplasms) and to determine whether it is useful for preoperative and postoperative evaluation. Retrospective data from 30 patients (age 2-16 yr) with posterior fossa tumor (17 low grade, 13 high grade) and 30 age-sex-matched healthy controls were used. Structural and diffusion-weighted images were collected at a 3-tesla scanner. Tractography was performed using Diffusion Toolkit software, Q-ball model, FACT algorithm, and angle threshold of 45 degrees. Manually assessed regions of interest were placed to identify reconstructed fiber pathways passing through the superior, medial, and inferior cerebellar peduncles for the preoperative, postoperative, and healthy control groups. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), and track volume measures were obtained and analyzed. Statistically significant differences were found between the preop/postop, preop/control, and postop/control comparisons for the volume of the tracts in both groups. Displacement and disruption of the pathways seemed to differ in relation to the severity of the tumor. The loss of pathways after the operation was associated with selective resection during surgery due to tumor infiltration. There were no FA differences but significantly higher ADC in low-grade tumors, and no difference in both FA and ADC in high-grade tumors. The effects of posterior fossa tumors on cerebellar peduncles and reconstructed pathways were successfully evaluated by HARDI tractography. The technique appears to be useful not only for preoperative but also for postoperative evaluation.

Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis

Background

In a considerable subgroup of glioma patients treated with (chemo) radiation new lesions develop either representing tumor progression (TP) or treatment-related abnormalities (TRA). Quantitative diffusion imaging metrics such as the Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) have been reported as potential metrics to noninvasively differentiate between these two phenomena. Variability in performance scores of these metrics and absence of a critical overview of the literature contribute to the lack of clinical implementation. This meta-analysis therefore critically reviewed the literature and meta-analyzed the performance scores.

Methods

Systematic searching was carried out in PubMed, EMBASE and The Cochrane Library. Using predefined criteria, papers were reviewed. Diagnostic accuracy values of suitable papers were meta-analyzed quantitatively.

Results

Of 1252 identified papers, 10 ADC papers, totaling 414 patients, and 4 FA papers, with 154 patients were eligible for meta-analysis. Mean ADC values of the patients in the TP/TRA groups were 1.13 × 10⁻³mm²/s (95% CI 0.912 × 10^–3–1.32 × 10⁻³mm²/s) and 1.38 × 10⁻³mm²/s (95% CI 1.33 × 10^–3–1.45 × 10⁻³mm²/s, respectively. Mean FA values of TP/TRA was 0.19 (95% CI 0.189–0.194) and 0.14 (95% CI 0.137–0.143) respectively. A significant mean difference between ADC and FA values in TP versus TRA was observed (p = 0.005).

Conclusions

Quantitative ADC and FA values could be useful for distinguishing TP from TRA on a meta-level. Further studies using serial imaging of individual patients are warranted to determine the role of diffusion imaging in glioma patients.

Muscle-invasive bladder cancer: pretreatment prediction of response to neoadjuvant chemotherapy with diffusion-weighted MR imaging

PURPOSE

To investigate the usefulness of diffusion-weighted MR imaging with ADC value and histogram analysis of ADC in the prediction of response to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC).

METHODS

Fifty-eight consecutive patients with clinical T2-4aN0M0 MIBC who underwent MRI before and after NAC were enrolled in the prospective study. The evaluation of response to NAC was based on the pathologic T (pT) stage after surgery. Patients with non-muscle-invasive residual cancer (pTa, pTis, pT1) were defined as responders, while those with muscle-invasive residual cancer (≥ pT2) were defined as non-responders. The ADC value measured from a single-section region of interest and ADC histogram parameters derived from whole-tumor volume of interest in responder and non-responder were compared using the Mann-Whitney U test or independent samples t test. ROC curve analysis was used to evaluate the diagnostic performance of ADC value and ADC histogram parameters in predicting the response to NAC.

RESULTS

The pretreatment ADC value of responders ([1.33 (± 0.21)] × 10^-3mm²/s) was significantly higher than that of non-responders ([1.09 (± 0.08)] × 10^-3mm²/s) (P < .001). Most of the pretreatment ADC histogram parameters (Mean, 10th, 25th, 50th, 75th, and 90th percentiles) of responders were significantly higher than that of non-responders (P < .001). The AUC was highest for the pretreatment ADC value (0.88; 95% confidence interval: 0.77, 0.95; P < .001).

CONCLUSION

Diffusion-weighted MR imaging with ADC value and histogram analysis of ADC are useful to predict NAC response in patients with MIBC.

Value of Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Prediction of Treatment Outcomes in Nasopharyngeal Carcinoma

OBJECTIVE

Magnetic resonance imaging (MRI) parameters that reflect the tumor microenvironment of nasopharyngeal carcinoma (NPC) may predict treatment response and facilitate treatment planning. This study aimed to evaluate the diffusion-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) values for predicting the treatment outcomes in NPC patients.

METHODS

Eighty-three patients with NPC underwent pretreatment MRI simulation with diffusion-weighted imaging and dynamic contrast-enhanced MRI. Average values of the apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, Vp, and tumor volume of the primary tumors were measured. Other potential clinical characteristics (age, sex, staging, pathology, pretreatment Epstein-Barr virus level, and treatment type) were analyzed. Patients underwent follow-up imaging 6 months after treatment initiation. Treatment responses were assigned according to the Response Evaluation Criteria in Solid Tumors guideline (version 1.1).

RESULTS

Fifty-one patients showed complete response (CR), whereas 32 patients did not (non-CR). Univariable logistic regression with variables dichotomized by optimal cutoff values showed that ADC ≥1.45 × 10-3 mm2/s, Vp ≥0.14, tumor volume of ≥14.05 mL, high stage (stages III and IV), and Epstein-Barr virus level of ≥2300 copies/mL were predictors of non-CR (P = 0.008, 0.05, 0.01, 0.009, and 0.04, respectively). The final multivariable model, consisting of a combination of ADC ≥1.45 × 10-3 mm2/s, Vp ≥0.14, and high stage, could predict non-CR with a good discrimination ability (area under the receiver operating characteristic curve, 0.76 [95% confidence interval, 0.66-0.87]; sensitivity, 62.50%; specificity, 80.39%; and accuracy 73.49%).

CONCLUSIONS

A multivariable prediction model using a combination of ADC ≥1.45 × 10-3 mm2/s, Vp ≥0.14, and high stage can be effective for treatment response prediction in NPC patients.

Volumetric apparent diffusion coefficient (ADC) histogram metrics as imaging biomarkers for pretreatment predicting response to fertility-sparing treatment in patients with endometrial cancer

OBJECTIVES

To investigate the feasibility of volumetric apparent diffusion coefficient (ADC) histogram analysis for prediction of fertility-sparing treatment (FST) response in patients with endometrial cancer (EC).

METHODS

Pretreatment data of 54 EC patients with FST were retrospectively analyzed. Treatment response at each follow-up was pathologically evaluated. The associations of ADC histogram metrics (volume, minADC, maxADC, meanADC; 10th, 25th, 50th, 75th and 90th ADC percentiles; skewness; kurtosis) and baseline clinical characteristics with complete response (CR) at the second and third follow-ups, two-consecutive CR, and recurrence at the final follow-up were evaluated by uni- and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used for diagnostic performance evaluation.

RESULTS

Compared with non-CR patients, CR patients had significantly higher minADC and 10th and 25th ADC percentiles at the second follow-up (P = 0.008, 0.039, and 0.034, respectively) and higher minADC, older age, lower HE4 level, and higher overweight rate at the third follow-up (P = 0.001, 0.040, 0.021, and 0.004, respectively). Patients with two-consecutive CR had a significantly higher minADC than those without (P = 0.018). There was no association between ADC metrics or clinical characteristics and recurrence (all P > 0.05). MinADC yielded the largest AUC in predicting CR (0.688 and 0.735 at the second and third follow-up, respectively) and the presence of two-consecutive CR (0.753). When combined with patient age and HE4 level, the prediction of CR could be further improved at the third follow-up, with an AUC of 0.786.

CONCLUSION

Pretreatment minADC could be a potential imaging biomarker for predicting FST response. Clinical characteristics may have incremental value to minADC in predicting CR.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI

RATIONALE AND OBJECTIVES

The study investigated the potential of the combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging in predicting the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) after two cycles of NAC.

MATERIALS AND METHODS

Eighty-seven patients with breast cancer who underwent MR examination before and after two cycles of NAC were enrolled. The patients were randomly assigned to a training cohort and a validation cohort (3:1 ratio). MRI parameters including tumor longest diameter, time-signal intensity curve, early enhanced ratio (E₉₀), maximal enhanced ratio and ADC value were measured, and percentage change in MRI parameters were calculated. Univariate analysis and multivariate logistic regression analysis were used to evaluate independent predictors of pCR in the training cohort. The validation cohort was used to test the prediction model, and the nomogram was created based on the prediction model.

RESULTS

This study demonstrated that the ADC value after two cycles of NAC (OR = 1.041, 95% CI (1.002, 1.081); p = 0.037), percentage decrease in E₉₀ (OR = 0.927, 95% CI (0.881, 0.977); p =0.004) and percentage decrease in tumor size (OR = 0.948, 95% CI (0.909, 0.988); p = 0.011) were significantly important for independently predicting pCR. The prediction model yielded AUC of 0.939 and 0.944 in the training cohort and the validation cohort, respectively.

CONCLUSION

The combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging could accurately predict pCR after two cycles of NAC. The prediction model and the nomogram had strong predictive value to NAC.

Pretreatment ADC predicts tumor control after Gamma Knife radiosurgery in solid vestibular schwannomas

BACKGROUND

Radiosurgery is a well-established treatment for vestibular schwannomas (VSs), but it is often difficult to identify which tumors will respond to treatment. We sought to determine whether pretreatment or posttreatment tumor apparent diffusion coefficient (ADC) values could predict tumor control in patients undergoing Gamma Knife radiosurgery (GKRS) and whether these values could differentiate between cases of pseudoprogression and cases of true progression in the early posttreatment period.

METHODS

We retrospectively identified patients who underwent GKRS for solid VSs between June 2008 and November 2016 and who had a minimum follow-up of 36 months. Pretreatment and posttreatment minimum, mean, and maximum ADC values were measured for the whole tumor volume and were compared between patients with tumor control and those with tumor progression. In patients with early posttreatment tumor enlargement, ADC values were compared between patients with pseudoprogression and those with true progression.

RESULTS

Of the 44 study patients, 34 (77.3%) demonstrated tumor control at final follow-up. Patients with tumor control had higher pretreatment minimum (1.35 vs 1.09; p = 0.008), mean (1.80 vs 1.45; p = 0.004), and maximum (2.41 vs 1.91; p = 0.011) ADC values than patients with tumor progression. ADC values did not differ between patients with pseudoprogression and those with true progression at early posttreatment follow-up.

CONCLUSIONS

ADC values may be helpful in predicting response to GKRS in patients with solid VSs but cannot predict which tumors will undergo pseudoprogression. Patients with higher pretreatment ADC values may be more likely to demonstrate posttreatment tumor control.

Effect Evaluation of Combined Application of Magnetic Resonance Diffusion Tensor Imaging and Brain Function Imaging in Radiation Therapy of Brain Tumours Involving Motor Pathways

This study is an attempt to find a way for functional imaging information to be applied clinically in radiation therapy. The basal nucleus is a collective term for a group of neural nucleus in the central nervous system that connects the pontine, brainstem, and cerebral cortex, including the caudate nucleus, the bean-shaped nucleus, the screen-shaped nucleus, and the amygdala. It is difficult to find the exact position of these neural nuclei on the computed tomography (CT) image or the T1 or T2 sequence of magnetic resonance. However, the development of neurosurgery has partially confirmed that these functional nuclei are involved in advanced cognitive functions such as memory, emotion, and learning. Neurosurgery has tried to avoid damaging these nucleus groups during surgery to improve the quality of life of patients, and there is currently no clear strategy for this in radiotherapy. Because CT and magnetic resonance spin echo (SE) sequences are difficult to find the anatomical location of the nucleus, it is difficult to have any strategy to protect these functions in radiotherapy planning. This article uses diffusion tensor imaging (DTI) images and fiber bundle tracking to obtain a more accurate anatomical position of the nerve nucleus on the image, and provides some available strategies for radiotherapy to protect patients’ brain function. The conclusion of this paper is that the combined application of DTI and functional magnetic resonance imaging (fMRI) can better observe the relationship among tumours, functional areas and white matter fibers, and guide the designation of radiotherapy plans.

Measurement of Apparent Diffusion Coefficient (ADC) Values of Ependymoma and Medulloblastoma Tumors: a Patient-based Study

Background:

Some brain tumors such as ependymoma and Medulloblastoma have similar MR images which may result to undifferentiated them from each other.

Objective:

This study aimed to compare the apparent diffusion coefficient (ADC) of two different cerebellar pediatric tumors, including ependymoma and medulloblastoma which have shown similar clinical images in conventional magnetic resonance imaging (MRI) methods.

Material and Methods:

In this analytical study, thirty six pediatric patients who were suspected to have the mentioned tumors according to their CT image findings were included in this study. The patients were subjected to conventional MRI protocols followed by diffusion weighted imaging (DWI) and ADC values of the tumors were calculated automatically using MRI scanner software.

Results:

The mean (± SD) ADC value for ependymoma (1.2± 0.06 ×10^-3 mm²/s) was significantly higher than medulloblastoma (0.87 ± 0.02 ×10^-3 mm²/s) (p = 0.041). Moreover, the maximum ADC value of ependymoma was considerably different in comparison with medulloblastoma (1.4 ×10^-3 mm²/s and 0.96×10^-3 mm²/s, respectively; p = 0.035). Furthermore, the minimum ADC value of ependymoma was higher compared to medulloblastoma (1.0 ×10^-3 mm²/s and 0.61×10^-3 mm²/s, respectively), but there was not significant (p = 0.067).

Conclusion:

Evaluation of ADC values for ependymoma and medulloblastoma is a reliable method to differentiate these two malignancies. This is due to different ADC values reflected during the evaluation.

Integrating baseline MR imaging biomarkers into BCLC and CLIP improves overall survival prediction of patients with hepatocellular carcinoma (HCC)

OBJECTIVES

We aimed to evaluate the independent predictive role of baseline imaging biomarkers for overall survival (OS) and transplant-free survival (TFS) in patients with HCC and assess the incremental value of these biomarkers to current staging systems.

METHODS

In this retrospective IRB approved study, the clinical, laboratory, and imaging parameters of 304 HCC patients were collected. Cox regression model was utilized to identify the potential predictors of survival. Recursive partitioning test was utilized to identify the optimal ADC cutoff for stratifying patients' OS. Patients were stratified based on Barcelona Clinic Liver Cancer (BCLC) and Cancer of the Liver Italian Program (CLIP). Binary ADC value (above vs. below the cutoff) and tumor margin (well- vs. ill-defined) were integrated into BCLC and CLIP. OS and TFS was compared for patients based on standard criteria with and without imaging biomarkers.

RESULTS

At baseline, patients with low tumor ADC and well-defined tumor margin (favorable imaging biomarkers) had longer survival, as compared to those with high ADC and ill-defined tumor margin (unfavorable imaging biomarkers) (median OS of 43 months vs. 7 months, respectively) (p < 0.001). Tumor ADC and tumor margin remained strong independent predictors of survival after adjustment for demographics, BCLC and CLIP staging, and tumor burden. Incorporating ADC and tumor margin improved performance of OS prediction by 9% in BCLC group and 6% in CLIP group.

CONCLUSION

Incorporating ADC and tumor margin to current staging systems for HCC significantly improve prediction of OS and TFS of these criteria.

KEY POINTS

• ADC and tumor margin are predictors of overall survival in HCC patients, independent of clinical, laboratory, and other imaging variables. • Adding ADC and tumor margin improved the prognostic value of BCLC and CLIP criteria by 9% and 6%, respectively. • High ADC and ill-defined tumor margin at baseline predicted poor survival, regardless of patient's liver function and general health status.

Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs

The objective of this study was to explore radiomics features from longitudinal diffusion-weighted MRIs (DWIs) for pathologic treatment effect prediction in patients with localized soft tissue sarcoma (STS) undergoing hypofractionated preoperative radiotherapy (RT). Thirty patients with localized STS treated with preoperative hypofractionated RT were recruited to this longitudinal imaging study. DWIs were acquired at three time points using a 0.35 T MRI-guided radiotherapy system. Treatment effect score (TES) was obtained from the post-surgery pathology as a surrogate of treatment outcome. Patients were divided into two groups based on TES. Response prediction was first performed using a support vector machine (SVM) with only mean apparent diffusion coefficient (ADC) or delta ADC to serve as the benchmark. Radiomics features were then extracted from tumor ADC maps at each of the three time points. Logistic regression and SVM were constructed to predict the TES group using features selected by univariate analysis and sequential forward selection. Classification performance using SVM with features from different time points and with or without delta radiomics were evaluated. Prediction performance using only mean ADC or delta ADC was poor (area under the curve (AUC) < 0.7). For the radiomics study using features from all time points and corresponding delta radiomics, SVM significantly outperformed logistic regression (AUC of 0.91 ± 0.05 v.s. 0.85 ± 0.06). Prediction AUC values using single or multiple time points without delta radiomics were all below 0.74. Including delta radiomics of mid- or post-treatment relative to the baseline drastically boosted the prediction. In this work, an SVM model was built to predict the TES using radiomics features from longitudinal DWI. Based on this study, we found that use of mean ADC, delta ADC, or radiomics features alone was not sufficient for response prediction, and including delta radiomics features of mid- or post-treatment relative to the baseline can optimize the prediction of TES, a pathologic and clinical endpoint.

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.

Diffusion MRI outlined viable tumour volume beats GTV in intra-treatment stratification of outcome

BACKGROUND AND PURPOSE

In radiotherapy, treatment response is generally evaluated many weeks after end of the treatment course. If the treatment outcome could be predicted during radiotherapy better tumour control could be achieved through timely adaptation of the treatment strategy. In this study intra-treatment change based on the diffusion MRI outlined viable tumour volume (VTV) was assessed and compared to the standard GTV to study their outcome prediction capacity.

MATERIALS AND METHODS

Thirty-eight brain metastases from twenty-one cancer patients were analysed in this prospective trial. Diffusion and structural MRI was acquired on a 1 T machine before, during, and at follow-up 2-3 months after radiotherapy. The VTV was defined as a region with high cellularity using high b-value diffusion MRI scans. Further, the diffusivity of the VTV was derived as the apparent diffusion coefficient (ADC). Treatment outcome was determined using RECIST defined bounds in the T1W MRI follow-up scan. Longitudinal statistical analysis was performed using a linear mixed effect model.

RESULTS

The GTV declined in both responding and non-responding (significantly) tumours with inseparable rates during radiotherapy. The VTV volume fraction reduced significantly in the responding tumours only. The ADC of the VTV increased significantly in responding metastases whereas it decreased in non-responding metastases. Furthermore, no association between baseline tumour size or primary disease and outcome was observed.

CONCLUSION

GTV size change during radiotherapy is not a reliable predictor of outcome in brain metastases. On the other hand, change in the volume fraction of VTV and diffusivity of VTV shows ability to stratify treatment outcome.

Assessment of the percentage of apparent diffusion coefficient value changes as an early indicator of the response of colorectal hepatic metastases to chemotherapy

Background

Colorectal cancer is considered one of the most common causes of cancer-related deaths worldwide. We aim to evaluate the efficacy of DWI-MRI in predicting response to chemotherapy in this cohort.

The study included 30 lesions in 20 biopsy proven-colorectal cancer patients with hepatic metastasis larger than 1 cm. All patients underwent both triphasic CT with intravenous contrast, pre-chemotherapy MRI (axial T2 and DW sequences) which was repeated 21 days following chemotherapy. A follow-up CT was done 2 months later. The response of the lesions was evaluated using the RESCIST criteria. On MRI, the lesions corresponding to the ones chosen on CT were identified and the apparent diffusion coefficient (ADC) values of pre- and post-chemotherapy images were recorded and correlated with the CT results.

Results

In the study, 17 (56.7%) of the lesions showed response to chemotherapy while 13 (43.3%) were non-responding. There was no significant difference in pretreatment ADC values between responding and non-responding lesions (p = 0.14). The mean percentage increase in ADC values in responding lesions was 42% compared to 18% in non-responding lesions (p < 0.001). Lesions that showed less than 18% increase were all found to be non-responsive

Conclusion

DWI-MRI has an emerging role in early assessment of early treatment response that can be detected before morphological response for patients with hepatic metastasis from colorectal cancer. Based on our study, the use of 25 % as the cutoff point of percent difference in ADC for detection of non-responding lesions proved to be successful only 21 days after the 1st chemotherapy cycle.

Cerebral blood volume and apparent diffusion coefficient - Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma

BACKGROUND

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. The core of standard of care for newly diagnosed GBM was established in 2005 and includes maximum feasible surgical resection followed by radiation and temozolomide, with subsequent temozolomide with or without tumor-treating fields. Unfortunately, nearly all patients experience a recurrence. Bevacizumab (BV) is a commonly used second-line agent for such recurrences, but it has not been shown to impact overall survival, and short-term response is variable.

METHODS

We collected MRI perfusion and diffusion images from 54 subjects with recurrent GBM treated only with radiation and temozolomide. They were subsequently treated with BV. Using machine learning, we created a model to predict short term response (6 months) and overall survival. We set time thresholds to maximize the separation of responders/survivors versus non-responders/short survivors.

RESULTS

We were able to segregate 21 (68%) of 31 subjects into unlikely to respond categories based on Progression Free Survival at 6 months (PFS6) criteria. Twenty-two (69%) of 32 subjects could similarly be identified as unlikely to survive long using the machine learning algorithm.

CONCLUSION

With the use of machine learning techniques to evaluate imaging features derived from pre- and post-treatment multimodal MRI, it is possible to identify an important fraction of patients who are either highly unlikely to respond, or highly likely to respond. This can be helpful is selecting patients that either should or should not be treated with BV.

Quantitative evaluation of computed and voxelwise computed diffusion-weighted imaging in breast cancer

OBJECTIVES

To assess the value of computed diffusion-weighted imaging (cDWI) and voxelwise computed diffusion-weighted imaging (vcDWI) in breast cancer.

METHODS

This retrospective study involved 130 patients (age range, 25-70 years; mean age ± standard deviation, 48.6 ± 10.5 years) with 130 malignant lesions, who underwent MRI examinations, including a DWI sequence, prior to needle biopsy or surgery. cDWIs with higher b-values of 1500, 2000, 2500, 3000, 3500, and 4000 s/mm², and vcDWI were generated from measured (m) DWI with two lower b-values of 0/600, 0/800, or 0/1000 s/mm². The signal-to-noise ratio (SNR) and contrast ratio (CR) of all image sets were computed and compared among different DWIs by two experienced radiologists independently. To better compare the CR with the SNR, the CR value was multiplied by 100 (CR100).

RESULTS

The CR of vcDWI, and cDWIs, except for cDWI₁₀₀₀, differed significantly from that of measured diffusion-weighted imaging (mDWI) (cDWI₁₀₀₀: CR = 0.4904, p = 0.394; cDWI₁₅₀₀: CR = 0.5503, p = 0.006; cDWI₂₀₀₀: CR = 0.5889, p < 0.001; cDWI₂₅₀₀: CR = 0.6109, p < 0.001; cDWI₃₀₀₀: mean = 0.6214, p < 0.001; cDWI₃₅₀₀: CR = 0.6245, p < 0.001; cDWI₄₀₀₀: CR = 0.6228, p < 0.001). The vcDWI provided the highest CR, while the CRs of all cDWI image sets improved with increased b-values. The SNR of neither cDWI₁₀₀₀ nor vcDWI differed significantly from that of mDWI, but the mean SNRs of the remaining cDWIs were significantly lower than that of mDWI. The SNRs of cDWIs declined with increasing b-values, and the initial decrease at low b-values was steeper than the gradual attenuation at higher b-values; the CR₁₀₀ rose gradually, and the two converged on the b-value interval of 1500-2000 s/mm² .

CONCLUSIONS

The highest CR was achieved with vcDWI; this could be a promising approach easier detection of breast cancer.

ADVANCES IN KNOWLEDGE

This study comprehensively compared and evaluated the value of the emerging post-processing DWI techniques (including a set of cDWIs and vcDWI) in breast cancer.

The primary lesion apparent diffusion coefficient is a prognostic factor for locoregionally advanced nasopharyngeal carcinoma: a retrospective study

BACKGROUND

To explore prognostic value of the pre-treatment primary lesion apparent diffusion coefficient (ADC) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC).

METHODS

A total of 843 patients with newly diagnosed LA-NPC were enrolled from January 2011 to April 2014 and divided into two groups based on ADC values: the low-ADC group and high-ADC group. The 3-year local relapse-free survival (LRFS), distant metastasis free survival (DMFS), disease-free survival (DFS) and overall survival (OS) rates between two groups were compared using Kaplan-Meier curve, and Cox regression analyses were performed to test prognostic value of the pretreatment ADC in LA-NPC.

RESULTS

The cut-off value of the pretreatment ADC for predicting local relapse was 784.5 × 10^- 6 mm²/s (AUC [area under curve] = 0.604; sensitivity = 0.640; specificity = 0.574), thus patients were divided into low-ADC (< 784.5 × 10^- 6; n = 473) group and high-ADC (≥784.5 × 10^- 6; n = 370) group. The low-ADC group had significantly higher 3-year LRFS rate and DFS rate than the high-ADC group (LRFS: 96.2% vs. 91.4%, P = 0.003; DFS: 81.4% vs. 73.0%, P = 0.0056). Multivariate analysis showed that the pretreatment ADC is an independent prognostic factor for LRFS (HR, 2.04; 95% CI, 1.13-3.66; P = 0.017) and DFS (HR, 1.41; 95% CI, 1.04-1.89; P = 0.024).

CONCLUSIONS

The pretreatment ADC of the primary lesion is an independent prognostic factor for LRFS and DFS in LA-NPC patients.

Tractography for Surgical Neuro-Oncology Planning: Towards a Gold Standard

Magnetic resonance imaging tractography permits in vivo visualization of white matter structures. Aside from its academic value, tractography has been proven particularly useful to neurosurgeons for preoperative planning. Preoperative tractography permits both qualitative and quantitative analyses of tumor effects upon surrounding white matter, allowing the surgeon to specifically tailor their operative approach. Despite its benefits, there is controversy pertaining to methodology, implementation, and interpretation of results in this context. High-definition fiber tractography (HDFT) is one of several non-tensor tractography approaches permitting visualization of crossing white matter trajectories at high resolutions, dispensing with the well-known shortcomings of diffusion tensor imaging (DTI) tractography. In this article, we provide an overview of the advantages of HDFT in a neurosurgical context, derived from our considerable experience implementing the technique for academic and clinical purposes. We highlight nuances of qualitative and quantitative approaches to using HDFT for brain tumor surgery planning, and integration of tractography with complementary operative adjuncts, and consider areas requiring further research.

Early Diffusion-Weighted Imaging and Proton Magnetic Resonance Spectroscopy Features of Liver Transplanted Tumors Treated with Radiation in Rabbits: Correlation with Histopathology

In this study, we sought to determine how diffusion-weighted imaging (DWI) and proton magnetic resonance spectroscopy (¹H-MRS) features are associated with histopathological results, and explored the cellular mechanisms of DWI and ¹H-MRS in early radiosensitivity of transplanted liver tumors. VX2 tumors were implanted into the hind leg muscles of 60 New Zealand White Rabbits. All rabbits were randomly divided into ten subgroups according to treatment: irradiated or nonirradiated and according to different times postirradiation. Magnetic resonance scanning was then performed one day before irradiation and on days 1, 3, 5 and 7 postirradiation. Differences in tumor volume, apparent diffusion coefficient (ADC) value, choline/creatine ratio and lipid/creatine ratio, and their associations with histopathological findings, were assessed. Tumor volumes in the irradiated groups were smaller than control values, while ADC values increased gradually with time postirradiation; choline/creatine ratios were reduced while lipid/creatine ratios were larger compared to control values. Bax protein levels after irradiation increased with time. Interestingly, the ADC value and Bax-positive grade showed the same increasing trend (r = 0.900, P < 0.001). Additionally, choline/creatine and lipid/creatine ratios were respectively significantly associated with Bax-positive grade. Furthermore, significant associations of tumor volume with ADC value, choline/creatine ratio and lipid/creatine ratio were observed. These findings demonstrated that ADC value, choline/creatine ratio and lipid/creatine ratio, indicators of early radiosensitivity, are related to cell apoptosis.

In Vivo Imaging Markers for Prediction of Radiotherapy Response in Patients with Nasopharyngeal Carcinoma: RESOLVE DWI versus DKI

In this prospective study, we compared the performance of readout segmentation of long variable echo trains of diffusion-weighted imaging (RESOLVE DWI) and diffusion kurtosis imaging (DKI) for the prediction of radiotherapy response in patients with nasopharyngeal carcinoma (NPC). Forty-one patients with NPC were evaluated. All patients underwent conventional MRI, RESOLVE DWI and DKI, before and after radiotherapy. All patients underwent conventional MRI every 3 months until 1 year after radiotherapy. The patients were divided into response group (RG; 36/41 patients) and no-response group (NRG; 5/41 patients) based on follow-up results. DKI (the mean of kurtosis coefficient, Kmean and the mean of diffusion coefficient, Dmean) and RESOLVE DWI (the minimum apparent diffusion coefficient, ADC_min) parameters were calculated. Parameter values at the pre-treatment period, post-treatment period, and the percentage change between these 2 periods were obtained. All parameters differed between the RG and NRG groups except for the pretreatment Dmean and ADC_min. Kmean-post was considered as an independent predictor of local control, with 87.5% sensitivity and 91.3% specificity (optimal threshold = 0.30, AUC: 0.924; 95% CI, 0.83-1.00). Kmean-post values of DKI have the potential to be used as imaging biomarkers for the early evaluation of treatment effects of radiotherapy on NPC.

Diffusion MRI of cancer: From low to high b-values

Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.

Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients?

The potential of total choline (tCho), apparent diffusion coefficient (ADC) and tumor volume, both individually and in combination of all these three parameters (multi-parametric approach), was evaluated in predicting both pathological and clinical responses in 42 patients with locally advanced breast cancer (LABC) enrolled for neoadjuvant chemotherapy (NACT). Patients were sequentially examined by conventional MRI; diffusion weighted imaging and in vivo proton MR spectroscopy at 4 time points (pre-therapy, after I, II, and III NACT) at 1.5 T. Miller Payne grading system was used for pathological assessment of response. Of the 42 patients, 24 were pathological responders (pR) while 18 were pathological non-responders (pNR). Clinical response determination classified 26 patients as responders (cR) while 16 as non-responders (cNR). tCho and ADC showed significant changes after I NACT, however, MR measured tumor volume showed reduction only after II NACT both in pR and cR. After III NACT, the sensitivity to detect responders was highest for MR volume (83.3% for pR and 96.2% for cR) while the specificity was highest for ADC (76.5% for pR and 100% for cR). Combination of all three parameters exhibited lower sensitivity (66.7%) than MR volume for pR prediction, however, a moderate improvement was seen in specificity (58.8%). For the prediction of clinical response, multi-parametric approach showed 84.6% sensitivity with 100% specificity compared to MR volume (sensitivity 96.2%; specificity 80%). Kappa statistics demonstrated substantial agreement of clinical response with MR volume (k = 0.78) and with multi-parametric approach (k = 0.80) while moderate agreement was seen for tCho (k = 0.48) and ADC (k = 0.46). The values of k for tCho, MR volume and ADC were 0.31, 0.38, and 0.18 indicating fair, moderate, and slight agreement, respectively with pathological response. Moderate agreement (k = 0.44) was observed between clinical and pathological responses. Our study demonstrated that both tCho and ADC are strong predictors of assessment of early pathological and clinical responses. Multi-parametric approach yielded 100% specificity in predicting clinical response. Following III NACT, MR volume emerged as highly suitable predictor for both clinical and pathological assessments. PCA demonstrated separate clusters of pR vs. pNR and cR vs. cNR at post-therapy while with some overlap at pre-therapy.

Longitudinal Assessment of Intravoxel Incoherent Motion Diffusion Weighted Imaging in Evaluating the Radio-sensitivity of Nasopharyngeal Carcinoma Treated with Intensity-Modulated Radiation Therapy

Purpose

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI)was evaluated regarding its ability to preliminarily predict the short-term treatment response of nasopharyngeal carcinoma (NPC) following intensity-modulated radiation therapy.

Materials and Methods

IVIM-DWI with 14 b-factors (0-1,000 sec/mm²) was performed with a 3T MR system on 47 consecutive NPCs before, during (end of the 5th, 10th, 15th, 20th, and 25th fractions), and after fractional radiotherapy. IVIM parametrics (D, f, and D*) were calculated and compared to the baseline and xth fraction. Patients were categorized into responders and non-responders after radiotherapy. IVIM parametrics were also compared between subgroups.

Results

After fractional radiations, the D (except D₅ and D at the end of the 5th fraction) after radiations were larger than the baseline D₀ (p < 0.05), and the post-radiation D* (except D*₅ and D*10) were smaller than D*₀ (p < 0.05). f₀ was smaller than f₅ and f₁₀ (p < 0.001) but larger than f_end (p < 0.05). Furthermore, greater D₅, D₁₀, D₁₅, and f₁₀ coupled with smaller f₀, D*₂₀, and D*₂₅ were observed in responders than non-responders (all p < 0.01). Responders also presented larger ΔD₁₀, Δf₁₀, ΔD*₂₀, and δD*₂₀ than non-responders (p < 0.05). Receiver operating characteristic curve analysis indicated that the D₅, D*₂₀, and f₁₀ could better differentiate responders from non-responders.

Conclusion

IVIM-DWI could efficiently assess tumor treatment response to fractional radiotherapy and predict the radio-sensitivity for NPCs.

Examination of the predictive factors of the response to whole brain radiotherapy for brain metastases from lung cancer using MRI

Previous studies have been conducted on the prognostic factors for overall survival in patients with brain metastases (BMs) following whole brain radiotherapy (WBRT). However, there have been a small number of studies regarding the prognostic factors for the response of tumor to WBRT. The aim of the present study was to identify the predictive factors for the response to WBRT from the point of view of reduction of tumor using magnetic resonance imaging. A retrospective analysis of 62 patients with BMs from primary lung cancer treated with WBRT was undertaken. The effects of the following factors on the response to WBRT were evaluated: Age; sex; performance status; lactate dehydrogenase; pathology; existence of extracranial metastases; activity of extracranial disease; chemo-history; chest radiotherapy history; treatment term; γ-knife radiotherapy; diffusion weighted image signal intensity; tumor diameter; extent of edema and the edema/tumor (E/T) ratio. The association between the reduction of tumors and clinical factors was evaluated using logistic regression analysis. P<0.05 was considered to indicate a statistically significant difference. The overall response ratio of this cohort was 54.8%. In the univariate analysis, the response of tumors was associated with the presence of small cell lung carcinoma (SCLC; P=0.0007), an E/T ratio of ≥1.5 (P=0.048), and a median tumor diameter of <20 mm (P=0.014). In the multivariate analysis, the presence of SCLC [P=0.001; odds ratio (OR), 17.152), an E/T ratio of ≥1.5 (P=0.019; OR, 9.526), and the presence of extracranial metastases (P=0.031; OR, 4.875) were revealed to be independent predictive factors for the reduction of tumor. The following 3 factors were significantly associated with the response of tumors to WBRT: The presence of SCLC; an E/T ratio of ≥1.5; and the presence of extracranial metastases. The E/T ratio is a novel index that provides a simple and easy predictive method for use in a clinical setting.

Diffusion-weighted MR imaging in prediction of response to neoadjuvant chemotherapy in patients with breast cancer

This study aims to evaluate the potential of apparent diffusion coefficient (ADC) derived from diffusion-weighted MR imaging for predicting the treatment response to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Magnetic resonance imaging was performed prior to NACT and after two cycles of NACT. The correlation between mean ADC_pre values, mean ADC_post values, changes in ADC values and changes in tumor diameters after NACT was examined using Spearman rank correlation. A total of 164 breast cancers were enrolled in this study. Mean ADC_pre values of responders ([0.85 ± 0.16] × 10^-3 mm²/s) and non-responders ([0.84 ± 0.21] × 10^-3 mm²/s) had no significant difference (P = 0.759). While mean ADC_post value of responders was significantly higher than that of non-responders ([1.17 ± 0.37] × 10^-3 mm²/s vs. [1.01 ± 0.28] × 10^-3 mm²/s; P = 0.002). Both mean ADC_post values (r = 0.288, P = 0.000) and changes in mean ADC values (r = 0.222, P = 0.004) were positively correlated to changes in tumor diameter after NACT, except for mean ADC_pre values (r = 0.031, P = 0.695). Our results indicated that mean ADC_post values and changes in ADC values after NACT might be a biological marker for assessing the efficacy of chemotherapy.

MR-guided radiation therapy: transformative technology and its role in the central nervous system

This review article describes advancement of magnetic resonance imaging technologies in radiation therapy planning, guidance, and adaptation of brain tumors. The potential for MR-guided radiation therapy to improve outcomes and the challenges in its adoption are discussed.

Apparent diffusion coefficient maps obtained from high b value diffusion-weighted imaging in the preoperative evaluation of gliomas at 3T: comparison with standard b value diffusion-weighted imaging

OBJECTIVE

To assess whether ADC maps obtained from high b value DWI were more valuable in preoperatively evaluating the grade, Ki-67 index and outcome of gliomas.

METHODS

Sixty-three patients with gliomas, who underwent preoperative multi b value DWI at 3 T, were enrolled. The ADC₁₀₀₀, ADC₂₀₀₀ and ADC₃₀₀₀ maps were generated. Receiver operating characteristic analyses were conducted to determine the area under the curve (AUC) in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG). Pearson correlation coefficients (R value) were calculated to investigate the correlation between parameters with the Ki-67 proliferation index. Survival analysis was conducted by using Cox regression.

RESULTS

The AUC of the mean ADC₁₀₀₀ value (0.820) was lower than that of the mean ADC₂₀₀₀ value (0.847) and mean ADC₃₀₀₀ value (0.875) in differentiating HGG from LGG. The R value of the mean ADC₁₀₀₀ value (-0.499) was less negative than that of the mean ADC₂₀₀₀ value (-0.530) and mean ADC₃₀₀₀ value (-0.567). The mean ADC₃₀₀₀ value was an independent prognosis factor for gliomas (p = 0.008), while the mean ADC₁₀₀₀ and ADC₂₀₀₀ values were not.

CONCLUSION

ADC maps obtained from high b value DWI might be a better imaging biomarker in the preoperative evaluation of gliomas.

KEY POINTS

• ADC ₃₀₀₀ maps could improve the differentiation between HGG and LGG. • The mean ADC ₃₀₀₀ value had a closer correlation with the Ki-67 index. • The mean ADC ₃₀₀₀ value was an independent prognosis factor for gliomas.

Pre-operative assessment of residual disease in locally advanced breast cancer patients: A sequential study by quantitative diffusion weighted MRI as a function of therapy

PURPOSE

The potential of diffusion weighted imaging (DWI) in assessing pathologic response and surgical margins in locally advanced breast cancer patients (n=38) undergoing neoadjuvant chemotherapy was investigated.

METHODS

DWI was performed at pre-therapy (Tp0), after I (Tp1) and III (Tp3) NACT at 1.5T. Apparent diffusion coefficient (ADC) of whole tumor (ADC_WT), solid tumor (ADC_ST), intra-tumoral necrosis (ADC_Nec) was determined. Further, ADC of 6 consecutive shells (5mm thickness each) including tumor margin to outside tumor margins (OM1 to OM5) was calculated and the data analyzed to define surgical margins.

RESULTS

Of 38 patients, 6 were pathological complete responders (pCR), 19 partial responders (pPR) and 13 were non-responders (pNR). Significant increase was observed in ADC_ST and ADC_WT in pCR and pPR following therapy. Pre-therapy ADC was significantly lower in pCR compared to pPR and pNR indicating the heterogeneous nature of tumor which may affect drug perfusion and consequently the response. ADC of outside margins (OM1, OM2, and OM3) was significantly different among pCR, pPR and pNR at Tp3 which may serve as response predictive parameter. Further, at Tp3, ADC of outside margins (OM1, OM2, and OM3) was significantly lower compared to that seen at Tp0 in pCR, indicating the presence of residual disease in these shells.

CONCLUSION

Pre-surgery information may serve as a guide to define cancer free margins and the extent of residual disease which may be useful in planning breast conservation surgery.

Apparent diffusion coefficient changes predict survival after intra-arterial bevacizumab treatment in recurrent glioblastoma

PURPOSE

Superselective intra-arterial cerebral infusion (SIACI) of bevacizumab (BV) has emerged as a novel therapy in the treatment of recurrent glioblastoma (GB). This study assessed the use of apparent diffusion coefficient (ADC) in predicting length of survival after SIACI BV and overall survival in patients with recurrent GB.

METHODS

Sixty-five patients from a cohort enrolled in a phase I/II trial of SIACI BV for treatment of recurrent GB were retrospectively included in this analysis. MR imaging with a diffusion-weighted (DWI) sequence was performed before and after treatment. ROIs were manually delineated on ADC maps corresponding to the enhancing and non-enhancing portions of the tumor. Cox and logistic regression analyses were performed to determine which ADC values best predicted survival.

RESULTS

The change in minimum ADC in the enhancing portion of the tumor after SIACI BV therapy was associated with an increased risk of death (hazard ratio = 2.0, 95% confidence interval(CI) [1.04-3.79], p = 0.038), adjusting for age, tumor size, BV dose, and prior IV BV treatments. Similarly, the change in ADC after SIACI BV therapy was associated with greater likelihood of surviving less than 1 year after therapy (odds ratio = 7.0, 95% CI [1.08-45.7], p = 0.04). Having previously received IV BV was associated with increased risk of death (OR 18, 95% CI [1.8-180.0], p = 0.014).

CONCLUSION

In patients with recurrent GB treated with SIACI BV, the change in ADC value after treatment is predictive of overall survival.

Repeated diffusion MRI reveals earliest time point for stratification of radiotherapy response in brain metastases

An imaging biomarker for early prediction of treatment response potentially provides a non-invasive tool for better prognostics and individualized management of the disease. Radiotherapy (RT) response is generally related to changes in gross tumor volume manifesting months later. In this prospective study we investigated the apparent diffusion coefficient (ADC), perfusion fraction and pseudo diffusion coefficient derived from diffusion weighted MRI as potential early biomarkers for radiotherapy response of brain metastases. It was a particular aim to assess the optimal time point for acquiring the DW-MRI scan during the course of treatment, since to our knowledge this important question has not been addressed directly in previous studies. Twenty-nine metastases (N  =  29) from twenty-one patients, treated with whole-brain fractionated external beam RT were analyzed. Patients were scanned with a 1 T MRI system to acquire DW-, T2^*W-, T2W- and T1W scans, before start of RT, at each fraction and at follow up two to three months after RT. The DW-MRI parameters were derived using regions of interest based on high b-value images (b  =  800 s mm^-2). Both volumetric and RECIST criteria were applied for response evaluation. It was found that in non-responding metastases the mean ADC decreased and in responding metastases it increased. The volume based response proved to be far more consistently predictable by the ADC change found at fraction number 7 and later, compared to the linear response (RECIST). The perfusion fraction and pseudo diffusion coefficient did not show sufficient prognostic value with either response assessment criteria. In conclusion this study shows that the ADC derived using high b-values may be a reliable biomarker for early assessment of radiotherapy response for brain metastases patients. The earliest response stratification can be achieved using two DW-MRI scans, one pre-treatment and one at treatment day 7-9 (equivalent to 21 Gy).

Quantitative magnetic resonance imaging biomarkers in oncological clinical trials: Current techniques and standardization challenges

Radiological imaging plays an important role in oncological trials to provide imaging biomarkers for disease staging, stratifying patients, defining dose setting, and evaluating the safety and efficacy of new candidate drugs and innovative treatment. This paper reviews the techniques of most commonly used quantitative magnetic resonance imaging (qMRI) biomarkers (dynamic contrast enhanced, dynamic susceptibility contrast, and diffusion weighted imaging) and their applications in oncological trials. Challenges of incorporating qMRI biomarkers in oncological trials are discussed including understanding biological mechanisms revealed by MRI biomarkers, consideration of rigorous trial design and standardized implementation of qMRI protocols.

Diffusion MRI in early cancer therapeutic response assessment

Imaging biomarkers for the predictive assessment of treatment response in patients with cancer earlier than standard tumor volumetric metrics would provide new opportunities to individualize therapy. Diffusion-weighted MRI (DW-MRI), highly sensitive to microenvironmental alterations at the cellular level, has been evaluated extensively as a technique for the generation of quantitative and early imaging biomarkers of therapeutic response and clinical outcome. First demonstrated in a rodent tumor model, subsequent studies have shown that DW-MRI can be applied to many different solid tumors for the detection of changes in cellularity as measured indirectly by an increase in the apparent diffusion coefficient (ADC) of water molecules within the lesion. The introduction of quantitative DW-MRI into the treatment management of patients with cancer may aid physicians to individualize therapy, thereby minimizing unnecessary systemic toxicity associated with ineffective therapies, saving valuable time, reducing patient care costs and ultimately improving clinical outcome. This review covers the theoretical basis behind the application of DW-MRI to monitor therapeutic response in cancer, the analytical techniques used and the results obtained from various clinical studies that have demonstrated the efficacy of DW-MRI for the prediction of cancer treatment response. Copyright © 2016 John Wiley & Sons, Ltd.

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

BACKGROUND

Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear.

METHODS

Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models.

RESULTS

All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred.

CONCLUSION

Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.

Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer

Objective

To explore the predictive value of parameters derived from diffusion-weighted imaging (DWI) and contrast-enhanced (CE)-MRI at different time-points during neoadjuvant chemotherapy (NACT) in breast cancer.

Methods

Institutional review board approval and written, informed consent from 42 breast cancer patients were obtained. The patients were investigated before and at three different time-points during neoadjuvant chemotherapy (NACT) using tumour diameter and volume from CE-MRI and ADC values obtained from drawn 2D and segmented 3D regions of interest. Prediction of pathologic complete response (pCR) was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis.

Results

There was no significant difference between pathologic complete response and non-pCR in baseline size measures (p > 0.39). Diameter change was significantly different in pCR (p < 0.02) before the mid-therapy point. The best predictor was lesion diameter change observed before mid-therapy (AUC = 0.93). Segmented volume was not able to differentiate between pCR and non-pCR at any time-point. The ADC values from 3D-ROI were not significantly different from 2D data (p = 0.06). The best AUC (0.79) for pCR prediction using DWI was median ADC measured before mid-therapy of NACT.

Conclusions

The results of this study should be considered in NACT monitoring planning, especially in MRI protocol designing and time point selection.

Key Points

• Mid-therapy diameter changes are the best predictors of pCR in neoadjuvant chemotherapy.

• Volumetric measures are not strictly superior in therapy monitoring to lesion diameter.

• Size measures perform as a better predictor than ADC values.

A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy

An accurate and noninvasive method for assessing treatment response following radiotherapy is needed for both treatment monitoring and planning. Measurement of solid tumor volume alone is not sufficient for reliable early detection of therapeutic response, since changes in physiological and/or biomechanical properties can precede tumor volume change following therapy. In this study, we use magnetic resonance elastography to evaluate the treatment effect after radiotherapy in a murine brain tumor model. Shear modulus was calculated and compared between the delineated tumor region of interest (ROI) and its contralateral, mirrored counterpart. We also compared the shear modulus from both the irradiated and non-irradiated tumor and mirror ROIs longitudinally, sampling four time points spanning 9-19 d post tumor implant. Results showed that the tumor ROI had a lower shear modulus than that of the mirror ROI, independent of radiation. The shear modulus of the tumor ROI decreased over time for both the treated and untreated groups. By contrast, the shear modulus of the mirror ROI appeared to be relatively constant for the treated group, while an increasing trend was observed for the untreated group. The results provide insights into the tumor properties after radiation treatment and demonstrate the potential of using the mechanical properties of the tumor as a biomarker. In future studies, more closely spaced time points will be employed for detailed analysis of the radiation effect.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

Intravoxel incoherent motion MRI as a biomarker of sorafenib treatment for advanced hepatocellular carcinoma: a pilot study

Background

To evaluate the association between the therapeutic outcomes of sorafenib for advanced hepatocellular carcinoma (HCC) and the parameters of intravoxel incoherent motion (IVIM).

Methods

Nine patients were evaluated prospectively. All patients were Child-Pugh score A. The mean dimension of the lesion was 32 mm (range: 15–74 mm). MR images were obtained using a 1.5-Tesla superconductive MRI system. Diffusion-weighted imaging was performed under breath-holding using b-values of 0, 50, 100, 150, 200, 400, and 800 s/mm². The following IVIM parameters were calculated: apparent diffusion coefficient, true diffusion coefficient (DC), pseudo-diffusion coefficient, and perfusion fraction. MRI was performed before treatment and at 1, 2, and 4 weeks after beginning treatment. Tumor response at 4 weeks was assessed by CT or MRI using modified RECIST. IVIM parameters of the treatment responders and non-responders were compared.

Results

The DC of responders at baseline was significantly higher than that of the non-responders. The sensitivity and specificity, when a DC of 0.8 (10⁻³ mm²/s) or higher was considered to be a responder, were 100 % and 67 %, respectively. No significant differences were found in the other parameters between the responders and the non-responders. All IVIM parameters of the responders and non-responders did not change significantly after treatment.

Conclusion

The DC before treatment may be a useful parameter for predicting the therapeutic outcome of sorafenib for advanced HCC.

Normalized Apparent Diffusion Coefficient in the Prognostication of Patients with Glioblastoma Multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is known to have poor prognosis, with no available imaging marker that can predict survival at the time of diagnosis. Diffusion weighted images are used in characterisation of cellularity and necrosis of GBM. The purpose of this study was to assess whether pattern or degree of diffusion restriction could help in the prognostication of patients with GBM.

MATERIAL AND METHODS

We retrospectively analyzed 84 consecutive patients with confirmed GBM on biopsy or resection. The study was approved by the institutional ethics committee. The total volume of the tumor and total volume of tumor showing restricted diffusion were calculated. The lowest Apparent Diffusion Coefficient (ADC) in the region of the tumor and in the contralateral Normal Appearing White Matter were calculated in order to calculate the nADC. Treatment and follow-up data in these patients were recorded. Multivariate analsysis was completed to determine significant correlations between different variables and the survival of these patients.

RESULTS

Patient survival was significantly related to the age of the patient (p<0.0001; 95% CI-1.022-1.043) and the nADC value (p=0.014; 95% CI-0.269-0.860) in the tumor. The correlation coefficients of age and nADC with survival were -0.335 (p=0.002) and 0.390 (p<0.001), respectively. Kaplan Meier survival function, grouped by normalized Apparent Diffusion Coefficient cut off value of 0.75, was significant (p=0.007).

CONCLUSION

The survival of patients with GBM had small, but significant, correlations with the patient's age and nADC within the tumor.

Diffusion tensor imaging in evaluation of posterior fossa tumors in children on a 3T MRI scanner

Context:

Primary intracranial tumors in children are commonly located in the posterior fossa. Conventional MRI offers limited information regarding the histopathological type of tumor which is essential for better patient management.

Aims:

The purpose of the study was to evaluate the usefulness of advanced MR imaging techniques like diffusion tensor imaging (DTI) in distinguishing the various histopathological types of posterior fossa tumors in children.

Settings and Design:

DTI was performed on a 3T MRI scanner in 34 untreated children found to have posterior fossa lesions.

Materials and Methods:

Using third party software, various DTI parameters [apparent diffusion coefficient (ADC), fractional anisotropy (FA), radial diffusivity, planar index, spherical index, and linear index] were calculated for the lesion.

Statistical Analysis Used:

Data were subjected to statistical analysis [analysis of variance (ANOVA)] using SPSS 15.0 software.

Results:

We observed significant correlation (P < 0.01) between ADC mean and maximum, followed by radial diffusivity (RD) with the histopathological types of the lesions. Rest of the DTI parameters did not show any significant correlation in our study.

Conclusions:

The results of our study support the hypothesis that most cellular tumors and those with greater nuclear area like medulloblastoma would have the lowest ADC values, as compared to less cellular tumors like pilocytic astrocytoma.

Large-volume low apparent diffusion coefficient lesions predict poor survival in bevacizumab-treated glioblastoma patients

BACKGROUND

Glioblastomas treated with bevacizumab may develop low-signal apparent diffusion coefficient (low-ADC) lesions, which may reflect increased tumor cellularity or atypical necrosis. The purpose of this study was to examine the relationship between low-ADC lesions and overall survival (OS). We hypothesized that growing low-ADC lesions would be associated with shorter OS.

METHODS

We retrospectively identified 52 patients treated with bevacizumab for the first (n = 42, 81%) or later recurrence of primary glioblastoma, who had low-ADC lesions and 2 post-bevacizumab scans ≤90 days apart. Low-ADC lesion volumes were measured, and normalized 5th percentile histogram low-ADC values were recorded. Using OS as the primary endpoint, semiparametric Cox models were fitted to ascertain univariate and multivariate hazard ratios (HRs) with significance at P = .05.

RESULTS

Median OS was 9.1 months (95% CI = 7.2-14.3). At the second post-bevacizumab scan, the volume of the low-ADC lesion (median: 12.94 cm(3)) was inversely associated with OS, with larger volumes predicting shorter OS (HR = 1.014 [95% CI = 1.003-1.025], P = .009). The percent change in low-ADC volume (median: 6.8%) trended toward increased risk of death with growing volumes (P = .08). Normalized 5th percentile low-ADC value and its percent change were not associated with OS (P > .51). Also correlated with shorter OS were the pre-bevacizumab nonenhancing volume (P = .025), the first post-bevacizumab enhancing volume (P = .040), and the second post-bevacizumab enhancing volume (P = .004).

CONCLUSIONS

The volume of low-ADC lesions at the second post-bevacizumab scan predicted shorter OS. This suggests that low-ADC lesions may be considered important imaging markers and included in treatment decision algorithms.

Noninvasive Dynamic Imaging of Tumor Early Response to Nanoparticle-mediated Photothermal Therapy

In spite of rapidly increasing interest in the use of nanoparticle-mediated photothermal therapy (PTT) for treatment of different types of tumors, very little is known on early treatment-related changes in tumor response. Using graphene oxide (GO) as a model nanoparticle (NP), in this study, we tracked the changes in tumors after GO NP-mediated PTT by magnetic resonance imaging (MRI) and quantitatively identified MRI multiple parameters to assess the dynamic changes of MRI signal in tumor at different heating levels and duration. We found a time- and temperature-dependent dynamic change of the MRI signal intensity in intratumor microenvironment prior to any morphological change of tumor, mainly due to quick and effective eradication of tumor blood vessels. Based on the distribution of GO particles, we also demonstrated that NP-medited PTT caused heterogeneous thermal injury of tumor. Overall, these new findings provide not only a clinical-related method for non-invasive early tracking, identifying, and monitoring treatment response of NP-mediated PTT but also show a new vision for better understanding mechanisms of NP-mediated PTT.

Molecular Imaging to Plan Radiotherapy and Evaluate Its Efficacy

Molecular imaging plays a central role in the management of radiation oncology patients. Specific uses of imaging, particularly to plan radiotherapy and assess its efficacy, require an additional level of reproducibility and image quality beyond what is required for diagnostic imaging. Specific requirements include proper patient preparation, adequate technologist training, careful imaging protocol design, reliable scanner technology, reproducible software algorithms, and reliable data analysis methods. As uncertainty in target definition is arguably the greatest challenge facing radiation oncology, the greatest impact that molecular imaging can have may be in the reduction of interobserver variability in target volume delineation and in providing greater conformity between target volume boundaries and true tumor boundaries. Several automatic and semiautomatic contouring methods based on molecular imaging are available but still need sufficient validation to be widely adopted. Biologically conformal radiotherapy (dose painting) based on molecular imaging-assessed tumor heterogeneity is being investigated, but many challenges remain to fully exploring its potential. Molecular imaging also plays increasingly important roles in both early (during treatment) and late (after treatment) response assessment as both a predictive and a prognostic tool. Because of potentially confounding effects of radiation-induced inflammation, treatment response assessment requires careful interpretation. Although molecular imaging is already strongly embedded in radiotherapy, the path to widespread and all-inclusive use is still long. The lack of solid clinical evidence is the main impediment to broader use. Recommendations for practicing physicians are still rather scarce. (18)F-FDG PET/CT remains the main molecular imaging modality in radiation oncology applications. Although other molecular imaging options (e.g., proliferation imaging) are becoming more common, their widespread use is limited by lack of tracer availability and inadequate reimbursement models. With the increasing presence of molecular imaging in radiation oncology, special emphasis should be placed on adequate training of radiation oncology personnel to understand the potential, and particularly the limitations, of quantitative molecular imaging applications. Similarly, radiologists and nuclear medicine specialists should be sensitized to the special need of the radiation oncologist in terms of quantification and reproducibility. Furthermore, strong collaboration between radiation oncology, nuclear medicine/radiology, and medical physics teams is necessary, as optimal and safe use of molecular imaging can be ensured only within appropriate interdisciplinary teams.

Diffusion-weighted magnetic resonance imaging in predicting the radiosensitivity of cervical cancer

This study investigates the application value of diffusion-weighted magnetic resonance imaging in predicting cervical cancer radiosensitivity. Twenty-five patients who were newly diagnosed as cervical cancer and accepted simple radiotherapy were included in this study. Before external irradiation, 20 GY and at the end of irradiation, routine 1.5 T MRI and diffusion-weighted magnetic resonance imaging scanning were carried. Apparent diffusion coefficient (ADC) value of primary tumor was measured. Its correlation with tumor regression rate was analyzed. ADC values of before irradiation, 20 GY and at the end of irradiation was (0.93 ± 0.14) × 10(-3) mm(2)/s, (1.25 ± 0.17) × 10(-3) mm(2)/s and (1.55 ± 0.13) × 10(-3) mm(2)/s, respectively. There were statistical significant differences (P< 0.01). D-value of ADC values between before and 20 GY external irradiation was (0.33 ± 0.16) mm(2)/s. The tumor volume before and at the end of external irradiation were (37.48 ± 26.83) cm(3) and (4.41 ± 3.72) cm(3) respectively, with tumor regression rate of before and after external irradiation of (0.86 ± 0.11). ADC values of before irradiation, 20 GY and at the end of irradiation did not correlate with tumor regression rate. D-value of ADC values between before and 20 GY external irradiation positively correlated with tumor regression rate (r = 0.423, P = 0.035). ADC value of cervical cancer increased after radiotherapy and early changes of ADC value was positively correlated with tumor regression rate, thus, ADC value could be used as a potential prediction factor for cervical cancer radiosensitivity.

The role of diffusion-weighted MRI and (18)F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: a systematic review

After neoadjuvant radiochemotherapy (RCT) for locally advanced rectal cancer, 15-27% of the patients experience a pathological complete response (pCR). This observation raises the question as to whether invasive surgery could be avoided in a selected cohort of patients who obtain a clinical complete response after preoperative RCT. In this respect, there has been growing interest in functional imaging techniques to improve clinical response assessment. This systematic review focuses on the role of diffusion-weighted imaging (DWI) and (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) in the prediction of pCR after RCT for rectal cancer. A total of 14 publications on DWI and 25 on (18)F-FDG PET/CT were retrieved. Pooled analysis of individual patient data shows both imaging modalities have a low positive predictive value in the prediction of pCR (mean PPV of 54% and 39% for DWI- and (18)F-FDG PET/CT-based parameters respectively). Especially pre-RCT imaging is unable to predict pCR with overall accuracies of 68-72% for DWI and 44% for (18)F-FDG PET/CT. Qualitative DWI assessment 5-10weeks after the end of RCT may outperform apparent diffusion coefficient (ADC)-based DWI-parameters (overall accuracy of 87% vs. 74-78%). Although few data are available, early changes in FDG-uptake seem promising in the prediction of pCR and the role of (18)F-FDG PET/CT during RCT should be further investigated. Quantitative and qualitative (18)F-FDG PET/CT measurements are equally effective in the assessment of pCR after RCT. The major strength of DWI and (18)F-FDG PET/CT lies in the identification of non-responders who are not candidates for organ preservation. Up to now, DWI and (18)F-FDG PET/CT are not accurate enough to safely select patients for organ-sparing strategies. Future research must focus on the integration of functional imaging with clinical data and molecular biomarkers.