Pre- and post-radiotherapy MRI results as a predictive model for response in laryngeal carcinoma

The purpose was to determine if pre-radiotherapy (RT) and/or post-radiotherapy magnetic resonance (MR) imaging can predict response in patients with laryngeal carcinoma treated with RT. Pre- and post-RT MR examinations of 80 patients were retrospectively reviewed and associated with regard to local control. Pre-RT MR imaging parameters such as tumor involvement of specific laryngeal anatomic subsites including laryngeal cartilages and post-RT changes, i.e., complete resolution of the tumor or focal mass/asymmetric obliteration of laryngeal tissue and signal pattern on T2-weighted images, were evaluated. Local control was defined as absence of a recurrence at the primary site for 2 years. Local control rates based on pretreatment MR findings were 73% for low pre- RT risk-profile and 29% for high pre- RT risk-profile patients (p=0.0001). Based on posttreatment MR findings, local control rates were 100% score 1, 64% score 2, and 4% score 3 (p< 0.0001). Using post-RT T2-weighted images, significant association was found between differences in signal pattern and local control: 77% hypointense, 54% isointense and 15% hyperintense lesions (p<0.001). Differences between means of delay of post-MRI examination were significantly associated with regard to local control (p=0.003); recurrent tumors followed 5 months after RT were more easily detectable on MRI than recurrent tumors within 4 months after RT. Sensitivity, specificity, accuracy, negative and positive predictive values of post-RT score 3 were 96%, 76%, 83%, 98% and 66%. Pre- and post-RT MRI evaluation of the larynx can identify patients at high risk for developing local failure

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Background: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68). Conclusion: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation

Attenuation-Corrected vs. Nonattenuation-Corrected 2-Deoxy-2-[F-18]fluoro-d-glucose-Positron Emission Tomography in Oncology, A Systematic Review

Purpose: To perform a systematic review and meta-analysis to determine the diagnostic accuracy of attenuation-corrected (AC) vs. nonattenuation-corrected (NAC) 2-deoxy-2-[F-18] fluoro-D-glucose-positron emission tomography (FDG-PET) in oncological patients. Procedures: Following a comprehensive search of the literature, two reviewers independently assessed the methodological quality of eligible studies. The diagnostic value of AC was studied through its sensitivity/specificity compared to histology, and by comparing the relative lesion detection rate reported with NAC-PET vs. AC, for full-ring and dual-head coincidence PET (FRand DH-PET, respectively). Results: Twelve studies were included. For FR-PET, the pooled sensitivity/specificity on a patient basis was 64/97 % for AC and 62/99 % for NAC, respectively. Pooled lesion detection with NAC vs. AC was 98 % [95 % confidence interval (95 % CI): 96Y99%, n=1,012 lesions] for FR-PET, and 88 % (95 % CI:81Y94%, n=288 lesions) for DH-PET. Conclusions: Findings suggest similar sensitivity/specificity and lesion detection for NAC vs. AC FR-PET and significantly higher lesion detection for NAC vs. AC DH-PET

Impact of partial-volume correction in oncological PET studies:A systematic review and meta-analysis

Purpose: Positron-emission tomography can be useful in oncology for diagnosis, (re)staging, determining prognosis, and response assessment. However, partial-volume effects hamper accurate quantification of lesions &lt;2–3× the PET system’s spatial resolution, and the clinical impact of this is not evident. This systematic review provides an up-to-date overview of studies investigating the impact of partial-volume correction (PVC) in oncological PET studies.Methods: We searched in PubMed and Embase databases according to the PRISMA statement, including studies from inception till May 9, 2016. Two reviewers independently screened all abstracts and eligible full-text articles and performed quality assessment according to QUADAS-2 and QUIPS criteria. For a set of similar diagnostic studies, we statistically pooled the results using bivariate meta-regression.Results: Thirty-one studies were eligible for inclusion. Overall, study quality was good. For diagnosis and nodal staging, PVC yielded a strong trend of increased sensitivity at expense of specificity. Meta-analysis of six studies investigating diagnosis of pulmonary nodules (679 lesions) showed no significant change in diagnostic accuracy after PVC (p = 0.222). Prognostication was not improved for non-small cell lung cancer and esophageal cancer, whereas it did improve for head and neck cancer. Response assessment was not improved by PVC for (locally advanced) breast cancer or rectal cancer, and it worsened in metastatic colorectal cancer.Conclusions: The accumulated evidence to date does not support routine application of PVC in standard clinical PET practice. Consensus on the preferred PVC methodology in oncological PET should be reached. Partial-volume-corrected data should be used as adjuncts to, but not yet replacement for, uncorrected data.</p

Convolutional neural networks for automatic image quality control and EARL compliance of PET images

Background: Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and methods: 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using fivefold cross-validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. Results: In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion: The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by, e.g., adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential

Quantitative implications of the updated EARL 2019 PET-CT performance standards

Purpose Recently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET-CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1). Methods Thirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET-CT studies were used to derive four image datasets-the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4). Results We found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23-30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed. Conclusions Application of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data