Early Prediction of Tumor Response: A Future Strategy for Optimizing Cancer Treatment

Ueda, Shigeto; Sendo, Toshiaki

doi:10.5772/57120

Cited by 4 publications

(7 citation statements)

References 57 publications

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“…Nevertheless, small discrepancies between the computational and experimental quantitative metrics are attributed to the location and size of the tumor and surrounding healthy tissues. We have also measured the qualitative evaluation of an augmented PET image generated by the DCGAN model and another solid tumor with the same order of size in breast lesion [ 59 ]. As indicated in Figure 6 g,h, the results are only shown for solid tumors, which have been cut circularly.…”

Section: Resultsmentioning

confidence: 99%

“…Comparison between ( e ) a synthetic PET image generated by biomathematical modeling without implementing the DCGAN for a 2cm tumor and ( f ) another augmented PET image generated using DCGAN. ( g ) The solid tumor in panel ( f ) is also compared with ( h ) another image of solid tumor taken experimentally [ 59 ], showing excellent agreement in terms of quantitative assessments with SSIM and PSNR values and qualitative comparison of radiotracer uptake in solid tumor. SSIM and PSNR values are defined according to Equations (16) and (17), respectively.…”

Section: Figurementioning

confidence: 93%

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Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

Abazari

Soltani

Kashkooli

et al. 2022

Cancers

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No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in each of 10 different tumor microvascular networks. The interstitial fluid parameters and spatiotemporal distribution of 18F-FDG uptake in tumor and healthy tissues have been compared against previously published numerical and experimental studies, indicating the accuracy of the model. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the generated PET sample and the experimental one are 0.72 and 28.53, respectively. Our results demonstrate that a combination of biomathematical modeling and GAN-based augmentation models provides a robust framework for the non-invasive and accurate generation of synthetic PET images of solid tumors in different stages.

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Section: Resultsmentioning

confidence: 99%

Section: Figurementioning

confidence: 93%

Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

Abazari

Soltani

Kashkooli

et al. 2022

Cancers

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“…Additionally, we observed that the current clinically used techniques for monitoring efficacy, such as FDG-PET or CT, are of limited use in the acute temporal setting. In some of the animals we actually noted a transient increase in FDG uptake, consistent with the flare response seen at the onset of chemotherapy as a result of increased glucose uptake by inflammatory cells and/or energy demand of the apoptosis process (59). Additionally, recent studies have indicated that the oncogene activation status can regulate the internalization of [ 18 F]FDG (60), which can further confound the results with such imaging techniques.…”

Section: Discussionmentioning

confidence: 76%

Reporter nanoparticle that monitors its anticancer efficacy in real time

Kulkarni

Rao

Natarajan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The ability to monitor the efficacy of an anticancer treatment in real time can have a critical effect on the outcome. Currently, clinical readouts of efficacy rely on indirect or anatomic measurements, which occur over prolonged time scales postchemotherapy or postimmunotherapy and may not be concordant with the actual effect. Here we describe the biology-inspired engineering of a simple 2-in-1 reporter nanoparticle that not only delivers a cytotoxic or an immunotherapy payload to the tumor but also reports back on the efficacy in real time. The reporter nanoparticles are engineered from a novel two-staged stimuli-responsive polymeric material with an optimal ratio of an enzyme-cleavable drug or immunotherapy (effector elements) and a drug function-activatable reporter element. The spatiotemporally constrained delivery of the effector and the reporter elements in a single nanoparticle produces maximum signal enhancement due to the availability of the reporter element in the same cell as the drug, thereby effectively capturing the temporal apoptosis process. Using chemotherapy-sensitive and chemotherapy-resistant tumors in vivo, we show that the reporter nanoparticles can provide a real-time noninvasive readout of tumor response to chemotherapy. The reporter nanoparticle can also monitor the efficacy of immune checkpoint inhibition in melanoma. The self-reporting capability, for the first time to our knowledge, captures an anticancer nanoparticle in action in vivo.T he failure of anticancer therapy is a major cause of mortality (1). Although the current dogma underlying resistance is based on the Darwinian selection of mutations acquired over time under chemotherapy pressure, emerging evidence indicates that anticancer drugs can be rendered ineffective early on by intrinsic or adaptive resistance as a function of tumor heterogeneity (2-4). For example, response rates to first-line chemotherapy treatments in metastatic breast cancer patients range from a dismal 30% to 70%, and patients with disease progression need to be switched to a different drug (5). Similarly, about 40-60% of patients with a wild-type KRAS do not respond to cetuximab (6). The ability to detect early whether a treatment is working or not and to switch, if necessary, to a regimen that is effective can have a significant effect on the outcome as well as quality of life (7,8).Currently, tumor response to therapy is determined using techniques for direct anatomical measurements, such as computed tomography (CT) and magnetic resonance imaging, or indirectly using positron emission tomography with 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG-PET) to quantify metabolic activity. However, these techniques lack the sensitivity or specificity to enable very early response assessment, and often, clinicopathological and metabolic readouts can be discordant (5, 9, 10). In the case of immunotherapy, for example, a productive immune response (T cell infiltration) and the unimpeded growth of the tumor will both be manifest as progression on the conventional ...

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“…Imaging of specific biomarkers associated with cancer has been developed for earlier diagnosis of oncology patients. PET with 18 F‐fluorodeoxyglucose is a representative imaging technique that can image glucose metabolism of the tumor cells and thus can assess the treatment response . However, the high uptake of 18 F‐fluorodeoxyglucose is not tumor‐specific, which could also be seen in normal tissue or in benign disease‐like inflammation and then lead to false‐positive 18 F‐fluorodeoxyglucose‐PET interpretation …”

Section: Introductionmentioning

confidence: 99%

“…PET with 18 F-fluorodeoxyglucose is a representative imaging technique that can image glucose metabolism of the tumor cells and thus can assess the treatment response. 13,14 However, the high uptake of 18 F-fluorodeoxyglucose is not tumor-specific, which could also be seen in normal tissue or in benign disease-like inflammation and then lead to false-positive 18 F-fluorodeoxyglucose-PET interpretation. 15 Cell death, including apoptosis and necrosis, plays an important role in treatment of cancer.…”

Section: Introductionmentioning

confidence: 99%

Rhein‐based necrosis‐avid MRI contrast agents for early evaluation of tumor response to microwave ablation therapy

Zhang

Jin

et al. 2019

Magnetic Resonance in Med

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Purpose Early evaluation of tumor response to thermal ablation therapy can help identify untreated tumor cells and then perform repeated treatment as soon as possible. The purpose of this work was to explore the potential of rhein‐based necrosis‐avid contrast agents (NACAs) for early evaluation of tumor response to microwave ablation (MWA). Methods 3‐[4,5‐dimethylthiazol‐2‐yl]‐2,5‐diphenyl tetrazolium bromide (MTT) assay was performed to test the cytotoxicity of rhein‐based NACAs against HepG2 cells. Rat models of liver MWA were used for investigating the effectiveness of rhein‐based NACAs in imaging the MWA lesion, the optimal time period for post‐MWA MRI examination, and the metabolic behaviors of 68Ga‐labeled rhein‐based NACAs. Rat models of orthotopic liver W256 tumor MWA were used for investigating the time window of rhein‐based NACAs for imaging the MWA lesion, the effectiveness of these NACAs in distinguishing the residual tumor and the MWA lesion, and their feasibility in early evaluating the tumor response to MWA. Results Gadolinium 2,2',2''‐(10‐(2‐((4‐(4,5‐Dihydroxy‐9,10‐dioxo‐9,10‐dihydroanthracene‐2‐carboxamido)butyl)amino)‐2‐oxoethyl)‐1,4,7,10‐tetraazacyclododecane‐1,4,7‐triyl)triacetic acid (GdL2) showed low cytotoxicity and high quality in imaging the MWA region. The optimal time period for post‐MWA MRI examination using GdL2 was 2 to 24 h after the treatment. During 2.5 to 3.5 h postinjection, GdL2 can better visualize the MWA lesion in comparison with gadolinium 2‐[4,7,10‐tris(carboxymethyl)‐1,4,7,10‐tetraazacyclododec‐1‐yl]acetic acid (Gd‐DOTA), and the residual tumor would not be enhanced. The tumor response to MWA as evaluated by using GdL2‐enhanced MRI was consistent with histological examination. Conclusion GdL2 appears to be a promising NACA for the tumor response assessment after thermal ablation therapies.

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Early Prediction of Tumor Response: A Future Strategy for Optimizing Cancer Treatment

Cited by 4 publications

References 57 publications

Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

Reporter nanoparticle that monitors its anticancer efficacy in real time

Rhein‐based necrosis‐avid MRI contrast agents for early evaluation of tumor response to microwave ablation therapy

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