Browsing by Subject "Cancer imaging"

Now showing 1 - 6 of 6
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    A carbon-11 labeled imidazo[1,2- a]pyridine derivative as a new potential PET probe targeting PI3K/mTOR in cancer
    (e-Century Publishing, 2023-06-25) Liu, Wenqing; Ma, Wenjie; Wang, Min; Wang, Zhuangzhuang; Grega, Shaun D.; Zheng, Qi-Huang; Xu, Zhidong; Radiology and Imaging Sciences, School of Medicine
    The PI3K/Akt/mTOR pathway is frequently dysregulated in cancer due to its central role in cell growth, survival, and proliferation. Overactivation of the PI3K/Akt/mTOR pathway may occur through varying mechanisms including mutations, gene amplification, and upstream signaling events, ultimately resulting in cancer. Therefore, PI3K/Akt/mTOR pathway has emerged as an attractive target for cancer therapy and imaging. A promising approach to inhibit this pathway involves a simultaneous inhibition of both PI3K and mTOR using a dual inhibitor. Recently, a potent dual PI3K/mTOR inhibitor, 2,4-difluoro-N-(2-methoxy-5-(3-(5-(2-(4-methylpiperazin-1-yl)ethyl)-1,3,4-oxadiazol-2-yl)imidazo[1,2-a]pyridin-6-yl)pyridin-3-yl)benzenesulfonamide (7), was discovered and demonstrated excellent kinase selectivity IC50 (PI3K/mTOR) = 0.20/21 nM; good cellular growth inhibition IC50 (HCT-116 cell) = 10 nM, modest plasma clearance, and acceptable oral bioavailability. Expanding on this discovery, here we present the synthesis of the carbon-11 labeled imidazo[1,2-a]pyridine derivative 2,4-difluoro-N-(2-methoxy-5-(3-(5-(2-(4-[11C]methylpiperazin-1-yl)ethyl)-1,3,4-oxadiazol-2-yl)imidazo[1,2-a]pyridin-6-yl)pyridin-3-yl)benzenesulfonamide (N-[11C]7) as a new potential radiotracer for the biomedical imaging technique positron emission tomography (PET) imaging of PI3K/mTOR in cancer. The reference standard 7 and its N-demethylated precursor, 2,4-difluoro-N-(2-methoxy-5-(3-(5-(2-(piperazin-1-yl)ethyl)-1,3,4-oxadiazol-2-yl)imidazo[1,2-a]pyridin-6-yl)pyridin-3-yl)benzenesulfonamide (11), were synthesized in 7 and 8 steps with 10% and 7% overall chemical yield, respectively. N-[11C]7 was prepared from 11 using [11C]methyl triflate ([11C]CH3OTf) through N-11C-methylation and isolated by high-performance liquid chromatography (HPLC) and solid-phase extraction (SPE) formulation in 40-50% radiochemical yield decay corrected to end of bombardment (EOB) based on [11C]CO2. The radiochemical purity was > 99% and the molar activity (Am) at EOB was in the range of 296-555 GBq/µmol (n = 5).
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    Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data
    (Nature Publishing Group, 2020-10-20) Ho, Chang Y.; Kindler, John M.; Persohn, Scott; Kralik, Stephen F.; Robertson, Kent A.; Territo, Paul R.; Radiology and Imaging Sciences, School of Medicine
    We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sørensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sørensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue.
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    Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
    (Nature Research, 2020-05-12) Amgad, Mohamed; Stovgaard, Elisabeth Specht; Balslev, Eva; Thagaard, Jeppe; Chen, Weijie; Dudgeon, Sarah; Sharma, Ashish; Kerner, Jennifer K.; Denkert, Carsten; Yuan, Yinyin; AbdulJabbar, Khalid; Wienert, Stephan; Savas, Peter; Voorwerk, Leonie; Beck, Andrew H.; Madabhushi, Anant; Hartman, Johan; Sebastian, Manu M.; Horlings, Hugo M.; Hudeček, Jan; Ciompi, Francesco; Moore, David A.; Singh, Rajendra; Roblin, Elvire; Balancin, Marcelo Luiz; Mathieu, Marie-Christine; Lennerz, Jochen K.; Kirtani, Pawan; Chen, I-Chun; Braybrooke, Jeremy P.; Pruneri, Giancarlo; Demaria, Sandra; Adams, Sylvia; Schnitt, Stuart J.; Lakhani, Sunil R.; Rojo, Federico; Comerma, Laura; Badve, Sunil S.; Khojasteh, Mehrnoush; Symmans, W. Fraser; Sotiriou, Christos; Gonzalez-Ericsson, Paula; Pogue-Geile, Katherine L.; Kim, Rim S.; Rimm, David L.; Viale, Giuseppe; Hewitt, Stephen M.; Bartlett, John M. S.; Penault-Llorca, Frédérique; Goel, Shom; Lien, Huang-Chun; Loibl, Sibylle; Kos, Zuzana; Loi, Sherene; Hanna, Matthew G.; Michiels, Stefan; Kok, Marleen; Nielsen, Torsten O.; Lazar, Alexander J.; Bago-Horvath, Zsuzsanna; Kooreman, Loes F. S.; Van der Laak, Jeroen A.W. M.; Saltz, Joel; Gallas, Brandon D.; Kurkure, Uday; Barnes, Michael; Salgado, Roberto; Cooper, Lee A. D.; International Immuno-Oncology Biomarker Working Group; Pathology and Laboratory Medicine, School of Medicine
    Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
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    Statistical multiscale mapping of IDH1, MGMT, and microvascular proliferation in human brain tumors from multiparametric MR and spatially-registered core biopsy
    (Nature Research, 2019-11-19) Parker, Jason G.; Diller, Emily E.; Cao, Sha; Nelson, Jeremy T.; Yeom, Kristen; Ho, Chang; Lober, Robert; Radiology and Imaging Sciences, School of Medicine
    We propose a statistical multiscale mapping approach to identify microscopic and molecular heterogeneity across a tumor microenvironment using multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR followed by MR-guided stereotactic core biopsy. The locations of the biopsy cores were identified in the pre-surgical images using stereotactic bitmaps acquired during surgery. Feature matrices mapped the multiparametric voxel values in the vicinity of the biopsy cores to the pathologic outcome variables for each patient and logistic regression tested the individual and collective predictive power of the MR contrasts. A non-parametric weighted k-nearest neighbor classifier evaluated the feature matrices in a leave-one-out cross validation design across patients. Resulting class membership probabilities were converted to chi-square statistics to develop full-brain parametric maps, implementing Gaussian random field theory to estimate inter-voxel dependencies. Corrections for family-wise error rates were performed using Benjamini-Hochberg and random field theory, and the resulting accuracies were compared. The combination of all five image contrasts correlated with outcome (P < 10−4) for all four microscopic variables. The probabilistic mapping method using Benjamini-Hochberg generated statistically significant results (α ≤ 0.05) for three of the four dependent variables: (1) IDH1, (2) MGMT, and (3) microvascular proliferation, with an average classification accuracy of 0.984 ± 0.02 and an average classification sensitivity of 1.567% ± 0.967. The images corrected by random field theory demonstrated improved classification accuracy (0.989 ± 0.008) and classification sensitivity (5.967% ± 2.857) compared with Benjamini-Hochberg. Microscopic and molecular tumor properties can be assessed with statistical confidence across the brain from minimally-invasive, mp-MR.
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    The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
    (Springer Nature, 2025-03-01) Fathi Kazerooni, Anahita; Akbari, Hamed; Hu, Xiaoju; Bommineni, Vikas; Grigoriadis, Dimitris; Toorens, Erik; Sako, Chiharu; Mamourian, Elizabeth; Ballinger, Dominique; Sussman, Robyn; Singh, Ashish; Verginadis, Ioannis I.; Dahmane, Nadia; Koumenis, Constantinos; Binder, Zev A.; Bagley, Stephen J.; Mohan, Suyash; Hatzigeorgiou, Artemis; O'Rourke, Donald M.; Ganguly, Tapan; De, Subhajyoti; Bakas, Spyridon; Nasrallah, MacLean P.; Davatzikos, Christos; Pathology and Laboratory Medicine, School of Medicine
    Background: Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events. Methods: We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity. Results: Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas. Conclusions: This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.
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    The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states
    (Springer, 2022-08-03) Rasche, Leo; Schinke, Carolina; Maura , Francesco; Bauer , Michael A.; Ashby, Cody; Deshpande , Shayu; Poos , Alexandra M.; Zangari , Maurizio; Thanendrarajan, Sharmilan; Davies, Faith E.; Walker, Brian A.; Barlogie, Bart; Landgren, Ola; Morgan, Gareth J.; van Rhee, Frits; Weinhold , Niels; Medicine, School of Medicine
    Deciphering Multiple Myeloma evolution in the whole bone marrow is key to inform curative strategies. Here, we perform spatial-longitudinal whole-exome sequencing, including 140 samples collected from 24 Multiple Myeloma patients during up to 14 years. Applying imaging-guided sampling we observe three evolutionary patterns, including relapse driven by a single-cell expansion, competing/co-existing sub-clones, and unique sub-clones at distinct locations. While we do not find the unique relapse sub-clone in the baseline focal lesion(s), we show a close phylogenetic relationship between baseline focal lesions and relapse disease, highlighting focal lesions as hotspots of tumor evolution. In patients with ≥3 focal lesions on positron-emission-tomography at diagnosis, relapse is driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion is typically seen (p < 0.01). Notably, we observe resistant sub-clones that can be hidden over years, suggesting that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.