Early Prediction Of Treatment Response Research Articles

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas.

Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas. Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors. In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value < .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035. A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.

A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer

Changes of radiomic features over time in longitudinal images, delta radiomics, can potentially be used as a biomarker to predict treatment response. This study aims to develop a delta-radiomic process based on machine learning by (1) acquiring and registering longitudinal images, (2) segmenting and populating regions of interest (ROIs), (3) extracting radiomic features and calculating their changes (delta-radiomic features, DRFs), (4) reducing feature space and determining candidate DRFs showing treatment-induced changes, and (5) creating outcome prediction models using machine learning. This process was demonstrated by retrospectively analyzing daily non-contrast CTs acquired during routine CT-guided-chemoradiation therapy for 90 pancreatic cancer patients. A total of 2520 CT sets (28-daily-fractions-per-patient) along with their pathological response were analyzed. Over 1300 radiomic features were extracted from the segmented ROIs. Highly correlated DRFs were ruled out using Spearman correlations. Correlation between the selected DRFs and pathological response was established using linear-regression-models. T test and linear-mixed-effects-models were used to determine which DRFs changed significantly compared with first fraction. A Bayesian-regularization-neural-network was used to build a response prediction model. The model was trained using 50 patients and leave-one-out-cross-validation. Performance was judged using the area-under-ROC-curve. External independent validation was done using data from the remaining 40 patients. The results show that 13 DRFs passed the tests and demonstrated significant changes following 2–4 weeks of treatment. The best performing combination differentiating good versus bad responders (CV-AUC = 0.94) was obtained using normalized-entropy-to-standard-deviation-difference-(NESTD), kurtosis, and coarseness. With further studies using larger data sets, delta radiomics may develop into a biomarker for early prediction of treatment response.

Longitudinal PET Imaging to Monitor Treatment Efficacy by Liposomal Irinotecan in Orthotopic Patient-Derived Pancreatic Tumor Models of High and Low Hypoxia

PurposeHypoxia is linked to aggressiveness, resistance to therapy, and poor prognosis of pancreatic tumors. Liposomal irinotecan (nal-IRI, ONIVYDE®) has shown potential in reducing hypoxia in the HT29 colorectal cancer model, and here, we investigate its therapeutic activity and ability to modulate hypoxia in patient-derived orthotopic tumor models of pancreatic cancer.ProceduresMice were randomized into nal-IRI treated and untreated controls. Magnetic resonance imaging was used for monitoring treatment efficacy, positron emission tomography (PET) imaging with F-18-labelled fluoroazomycinarabinoside ([18F]FAZA) for tumor hypoxia quantification, and F-18-labelled fluorothymidine ([18F]FLT) for tumor cell proliferation.ResultsThe highly hypoxic OCIP51 tumors showed significant response following nal-IRI treatment compared with the less hypoxic OCIP19 tumors. [18F]FAZA-PET detected significant hypoxia reduction in treated OCIP51 tumors, 8 days before significant changes in tumor volume. OCIP19 tumors also responded to therapy, although tumor volume control was not accompanied by any reduction in [18F]FAZA uptake. In both models, no differences were observable in [18F]FLT uptake in treated tumors compared with control mice.ConclusionsHypoxia modulation may play a role in nal-IRI’s mechanism of action. Nal-IRI demonstrated greater anti-tumor activity in the more aggressive and hypoxic tumor model. Furthermore, hypoxia imaging provided early prediction of treatment response.

Early Changes of Diffusion MRI Predict Radiation Therapy Effects in Esophageal Cancer

Concurrent chemoradiation (CRT) is the standard therapy for inoperable locally advanced esophageal cancer (LAEC). However, patients who are insensitive to CRT (20∼30%) may be harmed unnecessarily by the treatment toxicity without survival benefit. An early prediction of treatment response during therapy using non-invasive imaging could identify the patients at high-risk of poor response for individualized adaptive treatment. This study aimed to investigate physiological changes in LAEC using diffusion and perfusion MRI for early predicting treatment response to CRT. Sixty-seven LAEC patients (median age of 64 years) treated with CRT were enrolled in an IRB approved study. All patients received definite thoracic radiotherapy (RT) with a median dose of 60Gy and 30fx, and 73% received concurrent chemotherapy. MRI scans were performed pre-RT and at week 3 during RT (mid-RT), including diffusion weighed images to derive apparent diffusion coefficient (ADC) and dynamic contrast-enhanced images to quantify the fractional blood volume (BV). Gross tumor volume (GTV) was delineated on T2 weighted images. Within the GTV, the hypercellularity volume (VHC) was defined as ADC <1.86x10-3 mm2/s, and the high BV volume (VHBV) as BV >18.2 ml/(100g). ROC curve was applied to assess the prediction abilities of the imaging metrics for CRT outcomes. Associations of the imaging metrics pre-RT and mid-RT and their changes during RT with progression-free survival (PFS) of patients were analyzed by Cox proportional hazards regression analysis. With a median follow-up period of 35.1 months (range 20–41 months), median PFS was 10.1 months. For all patients, the median pre-RT GTV was 28cc±3.2cc, with decreasing of -32.5% ±7.2% to 16.5cc±2.2cc at mid RT. Respectively, VHC and VHBV decreased from 6cc±1.1cc and 4.5cc±1.1cc at pre-RT to 4.1cc±0.7cc and 3.2cc±0.7cc at mid-RT. Smaller VHC at mid-RT (AUC=0.65, p=0.03) and more decreasing in VHC at mid-RT (AUC=0.7, p=0.007) were associated with longer PFS (Table 1). No significant predictive effects were shown in GTV and VHBV. This study suggests that VHC may represent aggressive subvolumes in LAEC. Further analysis will be carried out to correlate the changes in image-phenotype subvolumes with local failure to determine the radiation-resistant tumor subvolumes, which may benefit from treatment intensification.Abstract 1033; Table 1Univariable and multivariable Cox proportional hazards regression analysisCut-off valuesHR95%CIP valueHR95%CIP valueGTVPre-RT33.1cc1.590.88-2.880.13GTVMid-RT13.3cc1.060.63-1.740.89GTVchange-54%1.090.55-2.160.80VHCPre-RT7.3cc1.470.81-2.680.21VHCMid-RT1.1cc2.281.10-4.770.03*2.020.95-4.280.66VHCchange-63%1.950.99-3.820.05*1.680.85-3.330.14VHBVPre-RT10.4cc1.470.94-2.740.14VHBVMid-RT3.3cc1.290.71-2.340.39VHBVchange-54%1.450.75-2.790.27* Statistically significant. Open table in a new tab

Circulating Tumor Cell cluster phenotype allows monitoring response to treatment and predicts survival

Circulating tumor cells (CTCs) are putative markers of tumor prognosis and may serve to evaluate patient’s response to chemotherapy. CTCs are often detected as single cells but infrequently as clusters and are indicative of worse prognosis. In this study, we developed a short-term culture of nucleated blood cells which was applied to blood samples from breast, lung, esophageal and bladder cancer patients. Clusters of different degrees of compactness, classified as very tight, tight and loose were observed across various cancer types. These clusters show variable expression of cytokeratins. Cluster formation from blood samples obtained during the course of chemotherapy was found to be associated with disease progression and shorter overall survival. The short-term cultures offer a robust and highly reliable method for early prediction of treatment response in different cancer types.

Diffusion-Weighted Magnetic Resonance Imaging of the Breast: an Accurate Method for Measuring Early Response to Neoadjuvant Chemotherapy?

Accurate prediction of early treatment response in patients receiving neoadjuvant chemotherapy for breast cancer is essential to provide patients with the best care. Diffusion-weighted magnetic resonance imaging (DWI) provides a novel approach to distinguishing responders from non-responders. This article aims to review the literature regarding the accuracy of DWI in predicting patients who will respond to neoadjuvant chemotherapy at an early stage within their treatment pathway. The initial search yielded 11,171 articles. After careful review, 19 of these articles were found to adhere to the inclusion criteria. Despite the varied research methods and data analysis across the studies, 16 out of 19 of these studies (84%) demonstrated DWI to be an accurate predictor of neoadjuvant chemotherapy response. These results are promising; however, further multi-centre studies with larger sample sizes, homogenous imaging and treatment parameters and formal assessment of pathological response are required to accurately evaluate the use of DWI as an early predictor of neoadjuvant chemotherapy response in patients with breast cancer.

Dynamic Contrast-enhanced Area-detector CT vs Dynamic Contrast-enhanced Perfusion MRI vs FDG-PET/CT: Comparison of Utility for Quantitative Therapeutic Outcome Prediction for NSCLC Patients Undergoing Chemoradiotherapy.

Purpose:To directly compare the utility for therapeutic outcome prediction of dynamic first-pass contrast-enhanced (CE)-perfusion area-detector computed tomography (ADCT), MR imaging assessed with the same mathematical method and 2-[fluorine-18]-fluoro-2-deoxy-d-glucose–positron emission tomography combined with CT (PET/CT) for non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy.Materials and Methods:Forty-three consecutive stage IIIB NSCLC patients, consisting of 25 males (mean age ± standard deviation: 66.6 ± 8.7 years) and 18 females (66.4 ± 8.2 years) underwent PET/CT, dynamic CE-perfusion ADCT and MR imaging, chemoradiotherapy, and follow-up examination. In each patient, total, pulmonary arterial, and systemic arterial perfusions were calculated from both perfusion data and SUVmax on PET/CT, assessed for each targeted lesion, and averaged to determine final values. Receiver operating characteristics analyses were performed to compare the utility for distinguishing responders from non-responders using Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 criteria. Overall survival (OS) assessed with each index were compared between two groups by means of the Kaplan–Meier method followed by the log-rank test.Results:Area under the curve (Az) for total perfusion on ADCT was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Az of total perfusion on MR imaging was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Mean OS of responder and non-responder groups were significantly different for total and systemic arterial (P < 0.05) perfusion.Conclusion:Dynamic first-pass CE-perfusion ADCT and MR imaging as well as PET/CT are useful for early prediction of treatment response by NSCLC patients treated with chemoradiotherapy.

Early Prediction of Treatment Response in Newly Diagnosed Multiple Myeloma

Background: The introduction of several novel agents has led to an improvement in response rates and survival outcomes in patients with newly diagnosed multiple myeloma. Despite significant therapeutic advances a subset of patients with multiple myeloma do not achieve a deep response to first-line treatment and biomarkers are needed to identify those at risk.Methods: We studied 472 patients with newly diagnosed multiple myeloma who were treated in clinical practice (n = 280, training cohort) or on prospective clinical trials (n = 192, validation cohort) at Mayo Clinic between 12/2003 and 12/2015. All patients had measurable disease (M-spike ≥ 1.0 g/dL) and received treatment with novel agents. Serum M-spike and free light chains (FLC) were measured before and after the first treatment cycle. The outcome of interest was the best response to first-line treatment (evaluated using the International Myeloma Working Group Uniform Response Criteria). Failure to achieve a deep response was defined as not achieving a very good partial response or better. The serum parameters of interest were the relative decrease in M-spike and absolute free light chain difference (ΔFLC). The Wilcoxon signed-rank test was used to compare the serum parameters before and after the first treatment cycle. Logistic regression models were used to assess the associations between the change in serum parameters after the first treatment cycle and best response to first-line treatment. P-values below 0.05 were considered statistically significant.Results: The median age at diagnosis in the training and the validation cohort were 67 (32 - 94) and 66 years (41 - 86), respectively. One hundred eighty one (65%) and 103 patients (54%) were male, respectively. The three most common regimens in the training cohort were lenalidomide + dexamethasone, lenalidomide + cyclophosphamide + dexamethasone, and bortezomib + lenalidomide + dexamethasone. In the validation cohort, the three most common regimens were carfilzomib + thalidomide + cyclophosphamide, lenalidomide + cyclophosphamide + dexamethasone, and ixazomib + cyclophosphamide + dexamethasone. In the two cohorts, the median baseline M-spike decreased from 3.1 g/dL (1.0 - 10.0) to 1.7 g/dL (0.0 - 6.6) and 3.2 g/dL (1.0 - 8.5) to 1.4 g/dL (0.0 - 4.5) after the first treatment cycle, respectively (p < 0.001 for both comparisons). The median baseline ΔFLC decreased from 31.1 mg/dL (0.0 - 2269.7) to 5.4 mg/dL (0.0 - 785.8) and from 23.3 mg/dL (0.1 - 3579.6) to 4.3 mg/dL (0.0 - 1139.3) after the first treatment cycle, respectively (p < 0.001 for both comparisons). M-spike reduction < 50% during the first treatment cycle was associated with failure to achieve a deep response: OR 4.54 (95% CI 2.72 - 7.58, p < 0.001) in the training cohort and OR 6.68 (95% CI 3.42 - 13.03, p < 0.001) in the validation cohort. Patients with reduction in M-spike < 10% during the first treatment cycle experienced failure to achieve a deep response in 86% (25/29) and 100% (10/10), respectively. ΔFLC reduction < 50% during the first treatment cycle was associated with treatment failure to achieve a deep response: OR 4.83 (95% CI 2.83 - 8.25, p < 0.001) in the training cohort and OR 5.09 (95% CI 2.37 - 10.92, p < 0.001) in the validation cohort. Patients with reduction in ΔFLC < 10% during the first treatment cycle experienced failure to achieve a deep response in 83% (34/41) and 91% (19/21), respectively.Conclusions: Early changes in M-spike and ΔFLC are strong predictors of response to treatment. We established and prospectively validated two readily available biomarkers that can identify patients at risk for treatment failure and may inform treatment decisions in newly diagnosed multiple myeloma. DisclosuresLacy:Celgene: Research Funding. Gertz:Physicians Education Resource: Consultancy; janssen: Consultancy; Teva: Consultancy; Alnylam: Honoraria; Research to Practice: Consultancy; Apellis: Consultancy; Medscape: Consultancy; annexon: Consultancy; Abbvie: Consultancy; celgene: Consultancy; Prothena: Honoraria; Ionis: Honoraria; Amgen: Consultancy; spectrum: Consultancy, Honoraria. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Russell:Vyriad: Equity Ownership. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Role of DW-MRI and ADC value in monitoring therapy of head and neck squamous cell carcinoma

AimTo evaluate role of DW-MRI and ADC value in monitoring therapy of head and neck squamous cell carcinoma (HNSCC). Patients & methodsFourty patients with head and neck squamous cell carcinoma, age ranged from 40 to 68 years, 30 patients were male while 10 were female. Pre-treatment examinations included contrast-enhanced CT, endoscopic biopsy & MRI study. Pre-treatment 1st DW-MRI imaging within 10 days before treatment (ADC1), 2nd imaging 3 weeks after start of treatment (ADC2) and 3rd after 6–8 weeks from end of treatment. ResultsSignificant changes between mean ADC value of 40 primary lesions & 22 metastatic LNs, noted at ADC1 and ADC2, indicating high ability of DW-MRI to detect early changes occur after beginning of treatment. Relationship between pretreatment ADC value and prediction of early treatment response revealed 76.9% sensitivity, 71.4% specificity, 83.3% PPV and 62.5% NPV. ROC curve for fractional ADC value change (ADC2 - ADC1) from 40 lesions primary tumors provided best discriminatory accuracy (AUC = 0.85 ± 0.09) in distinguishing between responders and non-responders with 92.3% sensitivity, 85.7% specificity, 92.9% PPV and 85.7% NPV. ConclusionIntra treatment ADC value can be used as a marker for prediction and monitoring therapy response for HNSCC.

A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy

AbstractBackgroundThe success of radiation therapy for cancer patients is dependent on the ability to deliver a total tumouricidal radiation dose capable of eradicating all cancer cells within the clinical target volume, however, the radiation dose tolerance of the surrounding healthy tissues becomes the main dose-limiting factor. The normal tissue adverse effects following radiotherapy are common and significantly impact the quality of life of patients. The likelihood of developing these adverse effects following radiotherapy cannot be predicted based only on the radiation treatment parameters. However, there is evidence to suggest that some common genetic variants are associated with radiotherapy response and the risk of developing adverse effects. Radiation genomics is a field that has evolved in recent years investigating the association between patient genomic data and the response to radiation therapy. This field aims to identify genetic markers that are linked to individual radiosensitivity with the potential to predict the risk of developing adverse effects due to radiotherapy using patient genomic information. It also aims to determine the relative radioresponse of patients using their genetic information for the potential prediction of patient radiation treatment response.Methods and materialsThis paper reports on a review of recent studies in the field of radiation genomics investigating the association between genomic data and patients response to radiation therapy, including the investigation of the role of genetic variants on an individual’s predisposition to enhanced radiotherapy radiosensitivity or radioresponse.ConclusionThe potential for early prediction of treatment response and patient outcome is critical in cancer patients to make decisions regarding continuation, escalation, discontinuation, and/or change in treatment options to maximise patient survival while minimising adverse effects and maintaining patients’ quality of life.

Monitoring Serum VEGF in Neoadjuvant Chemotherapy for Patients with Triple-Negative Breast Cancer: A New Strategy for Early Prediction of Treatment Response and Patient Survival.

This study aimed to investigate the clinical utility of serum biomarker changes during neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC). A total of 303 patients with TNBC were included in this study. Serum samples were taken at three time points during NAC: baseline, prior to the third cycle, and prior to surgery. Luminex multibiomarker panel for 29 serum biomarkers was used to detect their correlation with NAC response. The predictive and prognostic value of each selected biomarker was then studied. Vascular endothelial growth factor (VEGF) was the only biomarker that correlated with treatment response, with a decreasing trend in pCR patients relative to non-pCR patients (p < .001). Univariable and multivariable analyses revealed that the relative change in VEGF prior to the third cycle of NAC had a remarkable predictive value for both pCR and pathological nonresponse with high sensitivity and specificity. VEGF was also independently correlated with disease-free survival. Our findings indicate that monitoring serum VEGF could help identify patients with different responses at an early time point of NAC and at varying risk of disease relapse. Serum VEGF may also serve as an alternative to traditional response-evaluating methodologies in tailoring and modifying the NAC strategy for both operable and advanced TNBCs. Neoadjuvant chemotherapy (NAC) followed by definitive surgery is a standard of care for locally advanced breast cancer. The identification of sensitive responders to neoadjuvant therapy is highly significant for breast cancer, especially triple-negative breast cancer (TNBC). Results of this study indicate that the monitoring of serum vascular endothelial growth factor (VEGF) could identify patients with favorable or poor responses at an early time point of NAC. Furthermore, the prediction power of VEGF was better than traditional response-evaluating methods. VEGF might serve as a complement or alternative to traditional imaging-based response-evaluating methodologies in tailoring systemic treatment strategies for both operable and advanced TNBCs.

Biomarkers for primary biliary cholangitis: current perspectives.

Primary biliary cholangitis (PBC) is a chronic progressive cholestatic disease characterized by destruction of small- and medium-sized intrahepatic bile ducts. It is no longer a rare disease, since many new asymptomatic cases are incidentally identified. Liver biopsy is diagnostically critical but not always feasible or practical to be performed. Many potential, noninvasive, markers have been proposed to replace liver biopsy and further provide the assessment of disease severity and ultimate prognosis. In this review, we evaluated serum biomarkers proposed for diagnosis, extent of fibrosis, disease prognosis and attempts for early prediction of treatment response. Older biochemical and immunological markers are presented along with recent reports including the role of microRNAs and promising results based on proteomics and metabolomics.

'No evidence of disease activity' - is it an appropriate surrogate in multiple sclerosis?

The increasing number of disease‐modifying treatments available for multiple sclerosis has broadened treatment options for patients, but also challenges clinicians to select the best therapy for each individual at the appropriate stage of the disease. Early prediction of treatment response still remains one of the main difficulties in the management of multiple sclerosis patients. The concept of ‘no evidence of disease activity’ (NEDA) has been proposed as a surrogate for treatment response based on the absence of relapses, disability progression and radiological activity. Although there are several apparently logical arguments for the NEDA approach, there are also some major concerns that have to be considered and that are not sufficiently addressed yet. Amongst others, each parameter's limitations are not eliminated solely by its use within a composite score, and the contribution of each parameter to NEDA is not well balanced, as the detection of, for example, a single new magnetic resonance imaging lesion is considered as significant as the occurrence of a severely disabling relapse. NEDA in its current form also neglects underlying pathophysiology of the disease, has not been shown to fulfil formal criteria of a surrogate marker and its prognostic value has not been sufficiently evidenced yet. From a clinical point of view, ‘evidence of disease activity’ seems the more relevant surrogate; however, its implications are even less clear than those of NEDA. Here, existing literature on NEDA is critically reviewed and improvements are discussed that value its potential use in clinical trials and, even more importantly, treatment decision making in daily routine.

Response to Pharmacological Treatment in Major Depression Predicted by Electroencephalographic Alpha Power - a Pilot Naturalistic Study.

Pharmacological treatment of depression is currently led by the trial and error principle mainly because of lack of reliable biomarkers. Earlier findings suggest that baseline alpha power and asymmetry could differentiate between responders and non-responders to specific antidepressants. The current study investigated quantitative electroencephalographic (QEEG) measures before and early in treatment as potential response predictors to various antidepressants in a naturalistic sample of depressed patients. We were aiming at developing markers for early prediction of treatment response based on different QEEG measures. EEG data from 25 depressed subjects were acquired at baseline and after one week of treatment. Mean and total alpha powers were calculated at eight electrode sites F3, F4, C3, C4, P3, P4, O1, O2. Response to treatment was defined as 50% decrease in MADRS score at week 4. Mean P3 alpha predicted response with sensitivity and specificity of 80%, positive and negative predictive values of 92.31% and 71.43%, respectively. The combined model of response prediction using mean baseline P3 alpha and mean week 1 C4 alpha values correctly identified 80% of the cases with sensitivity of 84.62%, and specificity of 71.43%. Simple QEEG measures (alpha power) acquired before initiation of antidepressant treatment could be useful in outcome prediction with an overall accuracy of about 80%. These findings add to the growing body of evidence that alpha power might be developed as a reliable biomarker for the prediction of antidepressant response.

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).

Preliminary 19F-MRS Study of Tumor Cell Proliferation with 3'-deoxy-3'-fluorothymidine and Its Metabolite (FLT-MP).

The thymidine analogue 3′-deoxy-3′-[18F]fluorothymidine, or [18F]fluorothymidine ([18F]FLT), is used to measure tumor cell proliferation with positron emission tomography (PET) imaging technology in nuclear medicine. FLT is phosphorylated by thymidine kinase 1 (TK1) and then trapped inside cells; it is not incorporated into DNA. Imaging with 18F-radiolabeled FLT is a noninvasive technique to visualize cellular proliferation in tumors. However, it is difficult to distinguish between [18F]FLT and its metabolites by PET imaging, and quantification has not been attempted using current imaging methods. In this study, we successfully acquired in vivo 19F spectra of natural or nonradioactive 3′-deoxy-3′-fluorothymidine ([19F]FLT) and its monophosphate metabolite (FLT-MP) in a tumor xenograft mouse model using 9.4T magnetic resonance imaging (MRI). This preliminary result demonstrates that 19F magnetic resonance spectroscopy (MRS) with FLT is suitable for the in vivo assessment of tumor aggressiveness and for early prediction of treatment response.

Dynamic contrast-enhanced perfusion area-detector CT assessed with various mathematical models: Its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT

PurposeTo directly compare the capability of dynamic first-pass contrast-enhanced (CE-) perfusion area-detector CT (ADCT) and PET/CT for early prediction of treatment response, disease progression and overall survival of non-small cell carcinoma (NSCLC) patients treated with chemoradiotherapy. Materials and methodsFifty-three consecutive Stage IIIB NSCLC patients who had undergone PET/CT, dynamic first-pass CE-perfusion ADCT, chemoradiotherapy, and follow-up examination were enrolled in this study. They were divided into two groups: 1) complete or partial response (CR+PR) and 2) stable or progressive disease (SD+PD). Pulmonary arterial and systemic arterial perfusions and total perfusion were assessed at targeted lesions with the dual-input maximum slope method, permeability surface and distribution volume with the Patlak plot method, tumor perfusion with the single-input maximum slope method, and SUVmax, and results were averaged to determine final values for each patient. Next, step-wise regression analysis was used to determine which indices were the most useful for predicting therapeutic effect. Finally, overall survival of responders and non-responders assessed by using the indices that had a significant effect on prediction of therapeutic outcome was statistically compared. ResultsThe step-wise regression test showed that therapeutic effect (r2=0.63, p=0.01) was significantly affected by the following three factors in order of magnitude of impact: systemic arterial perfusion, total perfusion, and SUVmax. Mean overall survival showed a significant difference for total perfusion (p=0.003) and systemic arterial perfusion (p=0.04). ConclusionDynamic first-pass CE-perfusion ADCT as well as PET/CT are useful for treatment response prediction in NSCLC patients treated with chemoradiotherapy.

The Role of Intravoxel Incoherent Motion MRI in Predicting Early Treatment Response to Chemoradiation for Metastatic Lymph Nodes in Nasopharyngeal Carcinoma.

Pilot studies have suggested potential clinical applications for intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in head and neck cancers. This study aimed to characterize metastatic lymph nodes using IVIM MRI, and to evaluate the role of IVIM MRI in the prediction of the early treatment response of lymph node metastasis from nasopharyngeal carcinoma (NPC). A total of 122 patients with metastatic lymph nodes from NPC underwent two MRI examinations, pre-treatment and post-treatment (at 4weeks and at ≥2years from the end of chemoradiotherapy). Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors version 1.1. Differences in the initial IVIM parameters [pure molecular diffusion (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f)] between nodes with a partial response (PR) and a complete response (CR) were analyzed in 102 patients after the exclusion of 20. The initial D*, D, and apparent diffusion coefficient (ADC) did not reveal a significant difference between nodes showing a PR or a CR. The mean initial f value was significantly higher in patients with a PR relative to patients with a CR (p=0.003), and its sensitivity and specificity in predicting treatment response to chemoradiotherapy were 86.7% and 100%, respectively. The present study indicated that the initial f value may be more accurate than the initial D*, D, and ADC in the early prediction of treatment response to chemoradiotherapy for metastatic lymph nodes in patients with NPC.

Diffusion-weighted MRI for Early Prediction of Treatment Response on Preoperative Chemoradiotherapy for Patients With Locally Advanced Rectal Cancer: A Feasibility Study.

This study investigates the predictive value of diffusion-weighted magnetic resonance imaging (DW-MRI) for good pathological response at different time points during and after preoperative chemoradiotherapy (CRT) in locally advanced rectal cancer. Preoperative CRT followed by total mesorectal excision (TME) is the standard of care for locally advanced rectal cancer. The use of standard radical surgery in good treatment responders after CRT is being questioned. Patients with locally advanced rectal adenocarcinoma were treated with preoperative CRT followed by surgery. DW-MRI scans were performed before CRT, during the third week of CRT, 4 weeks post-CRT and presurgery. Tumor apparent diffusion coefficient (ADC) values were acquired from the DW-MRI scans. After surgery the pathological tumor regression grade was assessed according to the classification by Mandard et al [Cancer. 1994;73:2680-2686]. Patients with pathological complete or near-complete response (tumor regression grade 1-2) were classified as good responders (GRs). Twenty-two patients participated of which 9 were GRs (41%). Pre-CRT ADC values were lower in good versus moderate/poor responders (P = 0.04). ADC values during CRT and four weeks post-CRT were higher in GR. ADC values presurgery did not differ between response groups. For all time points the relative ADC increase (ΔADC) compared to the ADC pre-CRT was higher in GR (P < 0.001). The ΔADC during CRT and four weeks post-CRT were the best predictive parameters for pathological good response. This study shows that DW-MRI is feasible to select good treatment responders during preoperative CRT for locally advanced rectal cancer.

A novel approach to assess the treatment response using Gaussian random field in PET.

The assessment of early therapeutic response to anticancer therapy is vital for treatment planning and patient management in clinic. With the development of personal treatment plan, the early treatment response, especially before any anatomically apparent changes after treatment, becomes urgent need in clinic. Positron emission tomography (PET) imaging serves an important role in clinical oncology for tumor detection, staging, and therapy response assessment. Many studies on therapy response involve interpretation of differences between two PET images, usually in terms of standardized uptake values (SUVs). However, the quantitative accuracy of this measurement is limited. This work proposes a statistically robust approach for therapy response assessment based on Gaussian random field (GRF) to provide a statistically more meaningful scale to evaluate therapy effects. The authors propose a new criterion for therapeutic assessment by incorporating image noise into traditional SUV method. An analytical method based on the approximate expressions of the Fisher information matrix was applied to model the variance of individual pixels in reconstructed images. A zero mean unit variance GRF under the null hypothesis (no response to therapy) was obtained by normalizing each pixel of the post-therapy image with the mean and standard deviation of the pretherapy image. The performance of the proposed method was evaluated by Monte Carlo simulation, where XCAT phantoms (128(2) pixels) with lesions of various diameters (2-6 mm), multiple tumor-to-background contrasts (3-10), and different changes in intensity (6.25%-30%) were used. The receiver operating characteristic curves and the corresponding areas under the curve were computed for both the proposed method and the traditional methods whose figure of merit is the percentage change of SUVs. The formula for the false positive rate (FPR) estimation was developed for the proposed therapy response assessment utilizing local average method based on random field. The accuracy of the estimation was validated in terms of Euler distance and correlation coefficient. It is shown that the performance of therapy response assessment is significantly improved by the introduction of variance with a higher area under the curve (97.3%) than SUVmean (91.4%) and SUVmax (82.0%). In addition, the FPR estimation serves as a good prediction for the specificity of the proposed method, consistent with simulation outcome with ∼1 correlation coefficient. In this work, the authors developed a method to evaluate therapy response from PET images, which were modeled as Gaussian random field. The digital phantom simulations demonstrated that the proposed method achieved a large reduction in statistical variability through incorporating knowledge of the variance of the original Gaussian random field. The proposed method has the potential to enable prediction of early treatment response and shows promise for application to clinical practice. In future work, the authors will report on the robustness of the estimation theory for application to clinical practice of therapy response evaluation, which pertains to binary discrimination tasks at a fixed location in the image such as detection of small and weak lesion.