treatment response assessment
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eJHaem ◽  
2022 ◽  
Author(s):  
Thomas Millard ◽  
Ian Chau ◽  
Sunil Iyengar ◽  
Dima El‐Sharkawi ◽  
David Cunningham ◽  
...  

2021 ◽  
Author(s):  
Lukas Lundholm ◽  
Mikael Montelius ◽  
Oscar Jalnefjord ◽  
Eva Forssell‐Aronsson ◽  
Maria Ljungberg

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 101
Author(s):  
Noémie Moreau ◽  
Caroline Rousseau ◽  
Constance Fourcade ◽  
Gianmarco Santini ◽  
Aislinn Brennan ◽  
...  

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients’ response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.


2021 ◽  
Author(s):  
Eleonora F. Spinazzi ◽  
Michael G. Argenziano ◽  
Pavan S. Upadhyayula ◽  
Matei A. Banu ◽  
Justin A. Neira ◽  
...  

ABSTRACTGlioblastoma, the most common primary brain malignancy, is invariably fatal. Systemic chemotherapy is ineffective mostly because of drug delivery limitations. To overcome this, we devised an internalized pump-catheter system for direct chronic convection-enhanced delivery (CED) into peritumoral brain tissue. Topotecan (TPT) by chronic CED in 5 patients with refractory glioblastoma selectively eliminated tumor cells without toxicity to normal brain. Large, stable drug distribution volumes were non-invasively monitored with MRI of co-infused gadolinium. Analysis of multiple radiographically localized biopsies taken before and after treatment showed a decreased proliferative tumor signature resulting in a shift to a slow-cycling mesenchymal/astrocytic-like population. Tumor microenvironment analysis showed an inflammatory response and preservation of neurons. This novel drug delivery strategy and innovative clinical trial paradigm overcomes current limitations in delivery and treatment response assessment as shown here for glioblastoma and is potentially applicable for other anti-glioma agents as well as other CNS diseases.


Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2057
Author(s):  
Ismaheel O. Lawal ◽  
Kgomotso M. G. Mokoala ◽  
Mankgopo M. Kgatle ◽  
Rudi A. J. O. Dierckx ◽  
Andor W. J. M. Glaudemans ◽  
...  

Invasive fungal disease (IFD) leads to increased mortality, morbidity, and costs of treatment in patients with immunosuppressive conditions. The definitive diagnosis of IFD relies on the isolation of the causative fungal agents through microscopy, culture, or nucleic acid testing in tissue samples obtained from the sites of the disease. Biopsy is not always feasible or safe to be undertaken in immunocompromised hosts at risk of IFD. Noninvasive diagnostic techniques are, therefore, needed for the diagnosis and treatment response assessment of IFD. The available techniques that identify fungal-specific antigens in biological samples for diagnosing IFD have variable sensitivity and specificity. They also have limited utility in response assessment. Imaging has, therefore, been applied for the noninvasive detection of IFD. Morphologic imaging with computed tomography (CT) and magnetic resonance imaging (MRI) is the most applied technique. These techniques are neither sufficiently sensitive nor specific for the early diagnosis of IFD. Morphologic changes evaluated by CT and MRI occur later in the disease course and during recovery after successful treatment. These modalities may, therefore, not be ideal for early diagnosis and early response to therapy determination. Radionuclide imaging allows for targeting the host response to pathogenic fungi or specific structures of the pathogen itself. This makes radionuclide imaging techniques suitable for the early diagnosis and treatment response assessment of IFD. In this review, we aimed to discuss the interplay of host immunity, immunosuppression, and the occurrence of IFD. We also discuss the currently available radionuclide probes that have been evaluated in preclinical and clinical studies for their ability to detect IFD.


2021 ◽  
Vol 11 ◽  
Author(s):  
Jungheum Cho ◽  
Young Jae Kim ◽  
Leonard Sunwoo ◽  
Gi Pyo Lee ◽  
Toan Quang Nguyen ◽  
...  

BackgroundAlthough accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning.MethodsWe included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed.ResultsIn the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (κ, 0.52) and substantial (κ, 0.68) for one-dimensional and volumetric, respectively. In the geographic test set, sensitivity was 87.7% (95% CI: 77.2%, 94.5%), mean DSC was 0.68 ± 0.20, and FP rate per scan was 1.9 for BM ≥ 5 mm. In the temporal test set #2, sensitivity was 94.7% (95% CI: 74.0%, 99.9%), mean DSC was 0.82 ± 0.20, and FP per scan was 0.5 (6/12) for BM ≥ 5 mm.ConclusionsOur CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.


2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv22-iv23
Author(s):  
Markand Patel ◽  
Dilina Rajapakse ◽  
Jian Ping Jen ◽  
Sara Meade ◽  
Helen Benghiat ◽  
...  

Abstract Aims Following stereotactic radiosurgery (SRS), brain metastases can increase in size in up to a third of cases. Conventional magnetic resonance imaging (MRI) has a limited role to distinguish between tumour recurrence and SRS-induced changes, which can impact patient management. Delayed contrast MRI treatment response assessment maps (TRAM) use the principle of contrast clearance seen in other tumours, where high vascularity shows a rapid rise in contrast as well as rapid clearance, whereas areas of damaged or low vascularity show accumulation of contrast. We aimed to assess the ability of delayed contrast MRI and multiparametric MRI techniques of diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) and MR spectroscopy (MRS) to distinguish between radiation-related effects and tumour tissue, as these techniques assess tissue physiological and metabolic information. Method A retrospective review was performed on 23 patients who had delayed contrast and multiparametric MRI between October 2018 to April 2020. Studies were restricted to cases with brain metastases enlarging post-SRS with uncertainty at the MDT meeting regarding progression or treatment-related change, impacting the patient’s management. MRI was performed at 3T including DWI, PWI, MRS with short and intermediate echo times, and 3D T1 MPRAGE at 3-5, 20-30 and 70-90 minutes after administration of intravenous contrast. Contrast clearance analysis was performed by selecting an enhancing region of interest (ROI), measuring signal intensities at the three different timepoints and taking apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) values from the ROI. Choline/Creatine values were calculated from a single-voxel (10 mm isotropic) encompassing the entire contrast-enhancing lesion. Outcome was established from MRI follow-up at 6 months, with a stable or responding lesion considered treatment-related changes and increase considered progression. Results Across 23 patients, 24 metastases were assessed. Two patients were excluded as appropriate follow-up was not available. Sites of primary tumours included breast (n=8), lung (n=6), melanoma (n=4), neuroendocrine tumour from the lung (n=2) and renal cell carcinoma (n=2). Mean age was 56 years and 50% were female. In this cohort, 59% (n=13) were classified as having radiation-related changes on follow-up. Delayed MRI contrast clearance between the 3-5 and 70-90 minute imaging was significantly higher in cases of progression (23.6% vs. 2.5% decrease, p<0.05), as were the rCBV and Cho/Cr ratio (rCBV 3.1 vs. 1.5 and Cho/Cr ratio 2.3 vs. 1.4, p<0.05). Accuracy, sensitivity and specificity of using TRAM alone (contrast clearance decrease of >0%) for progression was 63%/100%/38%, PWI alone (rCBV cut-off 2.0) yielded results of 77%/75%/79% and for both Cho/Cr ratio alone (cut-off 1.8) and combined with TRAM, it was 90%/88%/92%. Neuroradiologist assessment of all techniques was 95%/100%/92%. Conclusion This study shows the effectiveness of delayed contrast and multiparametric MRI for treatment response assessment in patients with brain metastases treated by SRS in clinical practice. Although a delayed contrast MRI study is a very sensitive tool for detecting tumour progression, it lacks specificity. The accuracy of differentiating between tumour and treatment-related effects increases when delayed contrast MRI is used in combination with other advanced techniques such as MRS. By combining all these techniques, neuroradiologists had the highest accuracy, sensitivity and specificity for detecting progression in post-SRS brain metastases.


2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv1-iv1
Author(s):  
Markand Patel ◽  
Jinfeng Zhan ◽  
Kal Natarajan ◽  
Robert Flintham ◽  
Nigel Davies ◽  
...  

Abstract Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.


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