Intratumoral heterogeneity characterized by pretreatment PET in non-small cell lung cancer patients predicts progression-free survival on EGFR tyrosine kinase inhibitor

Intratumoral heterogeneity has been suggested to be an important resistance mechanism leading to treatment failure. We hypothesized that radiologic images could be an alternative method for identification of tumor heterogeneity. We tested heterogeneity textural parameters on pretreatment FDG-PET/CT in order to assess the predictive value of target therapy. Recurred or metastatic non-small cell lung cancer (NSCLC) subjects with an activating EGFR mutation treated with either gefitinib or erlotinib were reviewed. An exploratory data set (n = 161) and a validation data set (n = 21) were evaluated, and eight parameters were selected for survival analysis. The optimal cutoff value was determined by the recursive partitioning method, and the predictive value was calculated using Harrell's C-index. Univariate analysis revealed that all eight parameters showed an increased hazard ratio (HR) for progression- free survival (PFS). The highest HR was 6.41 (P< 0.01) with co-occurrence (Co) entropy. Increased risk remained present after adjusting for initial stage, performance status (PS), and metabolic volume (MV) (aHR: 4.86, P< 0.01). Textural parameters were found to have an incremental predictive value of early EGFR tyrosine kinase inhibitor (TKI) failure compared to that of the base model of the stage and PS (C-index 0.596 vs. 0.662, P = 0.02, by Co entropy). Heterogeneity textural parameters acquired from pretreatment FDG-PET/CT are highly predictive factors for PFS of EGFR TKI in EGFR-mutated NSCLC patients. These parameters are easily applicable to the identification of a subpopulation at increased risk of early EGFR TKI failure. Correlation to genomic alteration should be determined in future studies.

Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis

Purpose The linkage between the genetic and phenotypic heterogeneity of the tumor has not been thoroughly evaluated. Herein, we investigated how the genetic and metabolic heterogeneity features of the tumor are associated with each other in head and neck squamous cell carcinoma (HNSC). We further assessed the prognostic significance of those features. Methods The mutant-allele tumor heterogeneity (MATH) score (n = 508), a genetic heterogeneity feature, and tumor glycolysis feature (GlycoS) (n = 503) were obtained from the HNSC dataset in the cancer genome atlas (TCGA). We identified matching patients (n = 33) who underwent 18F-fluorodeoxyglucose positron emission tomography (FDG PET) from the cancer imaging archive (TCIA) and obtained the following information from the primary tumor: metabolic, metabolic-volumetric, and metabolic heterogeneity features. The association between the genetic and metabolic features and their prognostic values were assessed. Results Tumor metabolic heterogeneity and metabolic-volumetric features showed a mild degree of association with MATH (n = 25, ρ = 0.4~0.5, P < 0.05 for all features). The patients with higher FDG PET features and MATH died sooner. Combination of MATH and tumor metabolic heterogeneity features showed a better stratification of prognosis than MATH. Also, higher MATH and GlycoS were associated with significantly worse overall survival (n = 499, P = 0.002 and 0.0001 for MATH and GlycoS, respectively). Furthermore, both MATH and GlycoS independently predicted overall survival after adjusting for clinicopathologic features and the other (P = 0.015 and 0.006, respectively). Conclusion Both tumor metabolic heterogeneity and metabolic-volumetric features assessed by FDG PET showed a mild degree of association with genetic heterogeneity in HNSC. Both metabolic and genetic heterogeneity features were predictive of survival and there was an additive prognostic value when the metabolic and genetic heterogeneity features were combined. Also, MATH and GlycoS were independent prognostic factors in HNSC; they can be used for precise prognostication once validated.This study was supported by the National Research Foundation of Korea (NRF) (NRF-2017R1D1A1B03035556, and NRF-2019M2D2A1A01058210), and the Ministry of Health and Welfare Korea (HI18C0886, and HI19C0339)

A pan-cancer analysis of the clinical and genetic portraits of somatostatin receptor expressing tumor as a potential target of peptide receptor imaging and therapy

Abstract Purpose Although somatostatin receptor (SST) is a promising theranostic target and is widely expressed in tumors of various organs, the indication for therapies targeting SST is limited to typical gastroenteropancreatic neuroendocrine tumors (NETs). Thus, broadening the scope of the current clinical application of peptide receptor radiotherapy (PRRT) can be supported by a better understanding of the landscape of SST-expressing tumors. Methods SST expression levels were assessed in data from The Cancer Genome Atlas across 10,701 subjects representing 32 cancer types. As the major target of PRRT is SST subtype 2 (SST2), correlation analyses between the pan-cancer profiles, including clinical and genetic features, and SST2 level were conducted. The median SST2 expression level of pheochromocytoma and paraganglioma (PCPG) samples was used as the threshold to define high-SST2 tumors. The prognostic value of SST2 in each cancer subtype was evaluated by using Cox proportional regression analysis. Results We constructed a resource of SST expression patterns associated with clinicopathologic features and genomic alterations. It provides an interactive tool to analyze SST expression patterns in various cancer types. As a result, eight of the 31 cancer subtypes other than PCPG had more than 5% of tumors with high-SST2 expression. Low-grade glioma (LGG) showed the highest proportion of high-SST2 tumors, followed by breast invasive carcinoma (BRCA). LGG showed different SST2 levels according to tumor grade and histology. IDH1 mutation was significantly associated with high-SST2 status. In BRCA, the SST2 level was different according to the hormone receptor status. High-SST2 status was significantly associated with good prognosis in LGG patients. High-SST2 status showed a trend for association with poor prognosis in triple-negative breast cancer subjects. Conclusion A broad range of SST2 expression was observed across diverse cancer subtypes. The SST2 expression level showed a significant association with genomic and clinical aspects across cancers, especially in LGG and BRCA. These findings extend our knowledge base to diversify the indications for PRRT as well as SST imaging

Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma

Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD). Methods: A deep learning model to predict cytolytic activity score (CytAct) using semi-automatically segmented tumors on FDG-PET trained by a publicly available dataset paired with tissue RNA sequencing (n = 93). This model was validated in two independent cohorts of LUAD: SNUH (n = 43) and The Cancer Genome Atlas (TCGA) cohort (n = 16). The model was applied to the immune checkpoint blockade (ICB) cohort, which consists of patients with metastatic LUAD who underwent ICB treatment (n = 29). Results: The predicted CytAct showed a positive correlation with CytAct of RNA sequencing in validation cohorts (Spearman rho = 0.32, p = 0.04 in SNUH cohort; spearman rho = 0.47, p = 0.07 in TCGA cohort). In ICB cohort, the higher predicted CytAct of individual lesion was associated with more decrement in tumor size after ICB treatment (Spearman rho = -0.54, p < 0.001). Higher minimum predicted CytAct in each patient associated with significantly prolonged progression free survival and overall survival (Hazard ratio 0.25, p = 0.001 and 0.18, p = 0.004, respectively). In patients with multiple lesions, ICB responders had significantly lower variance of predicted CytActs (p = 0.005). Conclusion: The deep learning model that predicts CytAct using FDG-PET of LUAD was validated in independent cohorts. Our approach may be used to noninvasively assess an immune profile and predict outcomes of LUAD patients treated with ICB.

Hyperbolic disc embedding of functional human brain connectomes using resting-state fMRI

© 2022 Massachusetts Institute of Technology.The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging, we constructed a scale-free binary graph for each subject, using internodal time series correlation of regions of interest as a proximity measure. The resulting network could be embedded onto manifolds of various curvatures and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using the S1 /ℍ2 model, we reduced the dimension of the network into two-dimensional hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the hyperbolic discs. Each individual disc revealed relevance with its anatomic counterpart and absence of center-spaced node. Using the hyperbolic distance on the S1 /ℍ2 model, we could detect the anomaly of network in autism spectrum disorder subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.N

Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging

Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies. Keywords: Parkinson's disease, FP-CIT, Deep learning, Deep neural network, SWED

Development of Amyloid PET Analysis Pipeline Using Deep Learning-Based Brain MRI Segmentation&mdash;A Comparative Validation Study

Amyloid positron emission tomography (PET) scan is clinically essential for the non-invasive assessment of the presence and spatial distribution of amyloid-beta deposition in subjects with cognitive impairment suspected to have been a result of Alzheimer&rsquo;s disease. Quantitative assessment can enhance the interpretation reliability of PET scan; however, its clinical application has been limited due to the complexity of preprocessing. This study introduces a novel deep-learning-based approach for SUVR quantification that simplifies the preprocessing step and significantly reduces the analysis time. Using two heterogeneous amyloid ligands, our proposed method successfully distinguished standardized uptake value ratio (SUVR) between amyloidosis-positive and negative groups. The proposed method&rsquo;s intra-class correlation coefficients were 0.97 and 0.99 against PETSurfer and PMOD, respectively. The difference of global SUVRs between the proposed method and PETSurfer or PMOD were 0.04 and &minus;0.02, which are clinically acceptable. The AUC-ROC exceeded 0.95 for three tools in the amyloid positive assessment. Moreover, the proposed method had the fastest processing time and had a low registration failure rate (1%). In conclusion, our proposed method calculates SUVR that is consistent with PETSurfer and PMOD, and has advantages of fast processing time and low registration failure rate. Therefore, PET quantification provided by our proposed method can be used in clinical practice

Exploring the link between essential tremor and Parkinson’s disease

Abstract Epidemiological studies have reported a link between essential tremor (ET) and Parkinson’s disease (PD). Recent studies have suggested ET as a possible neurodegenerative disease whose subgroup contained Lewy bodies in the brainstem, as in PD. PD with antedated ET (PDconv) might exhibit traits different from those of the pure form of ET or PD. This study aimed to unveil the interplay between PD and premorbid ET, which might be the core pathobiology that differentiates PDconv from PD. The study included 51 ET, 32 PDconv, and 95 PD patients who underwent positron emission tomography using 18F-N-(3-fluoropropyl)-2beta-carbon ethoxy-3beta-(4-iodophenyl) nortropane and 123I-meta-iodobenzylguanidine myocardial scintigraphy to analyze central dopaminergic and peripheral noradrenergic integrity. The results show that PDconv group followed the typical striatal pathology of PD but with a delay in noradrenergic impairment as it caught up with the denervating status of PD a few years after PD diagnosis. Whereas the two PD subtypes displayed similar patterns of presynaptic dopamine transporter deficits, ET patients maintained high densities in all subregions except thalamus. Presynaptic dopaminergic availability decreased in a linear or quadratic fashion across the three groups (ET vs. PDconv vs. PD). The age at onset and duration of ET did not differ between pure ET and PDconv patients and did not influence the striatal monoamine status. The myocardium in PDconv patients was initially less denervated than in PD patients, but it degenerated more rapidly. These findings suggest that PDconv could be a distinctive subclass in which the pathobiology of PD interacts with that of ET in the early phase of the disease