Multimodal Detection and Prognostic Modeling of Atypical Teratoid Rhabdoid Tumors Using Machine Learning and Time Series Analysis
DOI:
https://doi.org/10.63682/jns.v14i20S.5114Keywords:
Atypical Teratoid Rhabdoid Tumors, Multimodal Diagnostics, Advanced Machine Learning, Genomic Sequencing, GNN, VARMAX, Time Series ForecastingAbstract
Atypical Teratoid Rhabdoid Tumors (ATRT) present formidable diagnostic and prognostic challenges owing to their intricate morphological and molecular het- erogeneity levels. Existing diagnostic frameworks exhibit pronounced limitations, primarily due to their reliance on singular modalities and conventional ana- lytical paradigms, leading to suboptimal precision and delayed intervention. This study endeavors to address the diagnostic and prognostic inefficiencies by employing a sophisticated multimodal approach, amalgamating diverse medi- cal imaging and genomic sequencing modalities with advanced machine learning models and time series forecasting. We leveraged a synergistic integration of multiple modalities—MRI processed using Graph Neural Networks (GNN), CT scans analyzed through Recurrent Neural Networks (RNN), PET scans inter- preted via Generative Adversarial Networks (GAN), Genomic Sequencing probed by 1D Convolutional Neural Networks (CNN), and Diffusion Tensor Imaging (DTI) scrutinized through 3D CNNs. Each modality underwent individualized processing to extract nuanced characteristics of ATRT. Subsequently, the Vector AutoRegressive Moving Average with Exogenous Inputs (VARMAX) model was applied to each modality, enabling the forecasting of potential future brain dis- eases. The meticulous integration of these advanced methodologies culminated in substantial breakthroughs in various performance Indicators in the context of present models. Our framework demonstrated an increase in precision by 4.9%, an elevation in accuracy by 8.3%, an enhancement in recall by 8.5%, an aug- mentation in the AUC by 5.5%, an amplification in specificity by 2.9%, and a 3.9% reduction in delay. Furthermore, the deployment of VARMAX on indi- vidual modalities resulted in an additional 2.5% precision, 3.5% accuracy, 2.9% recall, 4.9% AUC, 3.4% specificity, and 1.9% reduction in prediction delays. This pioneering research stands as a beacon of refined diagnostic and prognostic resolution in the domain of ATRT, elucidating the transformative potential of multimodal fusion and advanced machine learning in mediating enhanced clinical outcomes. The notable enhancements in diagnostic metrics underscore the feasi- bility and efficacy of this comprehensive approach in early and accurate ATRT detection, facilitating timely and precise therapeutic interventions. Additionally, the prognostic advancements enabled by VARMAX provide pivotal insights into the potential evolution of brain diseases, aiding in the proactive management of pathological progressions. The integration of multimodal analytical paradigms with sophisticated machine learning and forecasting models has engendered a novel framework with substantial implications for the detection and prognosis of ATRT. This research bridges the existing diagnostic gaps and furnishes a robust platform for the exploration of similar integrative approaches in delineating other complex pathologies, thereby propelling the realms of computational diagnostics and personalized medicine towards unprecedented horizons
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Li, C.-H., Lim, S.-H., Jeong, Y.-I., Ryu, H.-H., Jung, S.: Synergistic effects of radiotherapy with jnk inhibitor-incorporated nanoparticle in an intracranial lewis lung carcinoma mouse models. IEEE Transactions on NanoBioscience 22(4), 845– 854 (2023)
Zhuang, Y., Liu, H., Song, E., Hung, C.-C.: A 3d cross-modality feature inter- action network with volumetric feature alignment for brain tumor and tissue segmentation. IEEE Journal of Biomedical and Health Informatics 27(1), 75–86 (2022) Lin, J., Lin, J., Lu, C., Chen, H., Lin, H., Zhao, B., Shi, Z., Qiu, B., Pan,
X., Xu, Z., et al.: Ckd-transbts: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation. IEEE transactions on medical imaging 42(8), 2451–2461 (2023)
Arjun, B., Alekya, B., Hari, R., Vikas, V., Mahadevan, A., Pandya, H.J.: Elec- tromechanical characterization of human brain tissues: A potential biomarker for tumor delineation. IEEE Transactions on Biomedical engineering 69(11), 3484–3493 (2022)
Subramanian, S., Ghafouri, A., Scheufele, K.M., Himthani, N., Davatzikos, C., Biros, G.: Ensemble inversion for brain tumor growth models with mass effect. IEEE transactions on medical imaging 42(4), 982–995 (2022)
Cheng, B., Bing, C., Chu, T.H., Alzahrani, S., Pichardo, S., Pike, G.B.: Simul- taneous localized brain mild hyperthermia and blood-brain barrier opening via feedback-controlled transcranial mr-guided focused ultrasound and microbubbles. IEEE Transactions on Biomedical Engineering 69(6), 1880–1888 (2021)
Zhao, J., Xing, Z., Chen, Z., Wan, L., Han, T., Fu, H., Zhu, L.: Uncertainty-aware multi-dimensional mutual learning for brain and brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics 27(9), 4362–4372 (2023)
Ottom, M.A., Rahman, H.A., Dinov, I.D.: Znet: deep learning approach for 2d mri brain tumor segmentation. IEEE Journal of Translational Engineering in Health and Medicine 10, 1–8 (2022)
Ding, Y., Zheng, W., Geng, J., Qin, Z., Choo, K.-K.R., Qin, Z., Hou, X.: Mvfusfra: A multi-view dynamic fusion framework for multimodal brain tumor segmen- tation. IEEE Journal of Biomedical and Health Informatics 26(4), 1570–1581 (2021)
Ramprasad, M., Rahman, M.Z.U., Bayleyegn, M.D.: Sbtc-net: Secured brain tumor segmentation and classification using black widow with genetic optimiza- tion in iomt. IEEE Access (2023)
Sekhar, A., Biswas, S., Hazra, R., Sunaniya, A.K., Mukherjee, A., Yang, L.: Brain tumor classification using fine-tuned googlenet features and machine learn- ing algorithms: Iomt enabled cad system. IEEE journal of biomedical and health informatics 26(3), 983–991 (2021)
Rodrigues, D.B., Ellsworth, J., Turner, P.: Feasibility of heating brain tumors using a 915 mhz annular phased-array. IEEE Antennas and Wireless Propagation Letters 20(4), 423–427 (2021)
Zhou, T., Canu, S., Vera, P., Ruan, S.: Latent correlation representation learning for brain tumor segmentation with missing mri modalities. IEEE Transactions on Image Processing 30, 4263–4274 (2021)
Chen, C., Raymond, C., Speier, W., Jin, X., Cloughesy, T.F., Enzmann, D., Ellingson, B.M., Arnold, C.W.: Synthesizing mr image contrast enhancement using 3d high-resolution convnets. IEEE Transactions on Biomedical Engineering 70(2), 401–412 (2022)
Solanki, S., Singh, U.P., Chouhan, S.S., Jain, S.: Brain tumor detection and classi- fication using intelligence techniques: an overview. IEEE Access 11, 12870–12886 (2023)
Alagarsamy, S., Govindaraj, V., Senthilkumar, A.: Automated brain tumor seg- mentation for mr brain images using artificial bee colony combined with interval type-ii fuzzy technique. IEEE Transactions on Industrial Informatics 19(11), 11150–11159 (2023)
Jabbar, A., Naseem, S., Mahmood, T., Saba, T., Alamri, F.S., Rehman, A.: Brain tumor detection and multi-grade segmentation through hybrid caps-vggnet model. IEEE Access 11, 72518–72536 (2023)
Yan, C., Ding, J., Zhang, H., Tong, K., Hua, B., Shi, S.: Seresu-net for multimodal brain tumor segmentation. IEEE Access 10, 117033–117044 (2022)
Yang, H., Zhou, T., Zhou, Y., Zhang, Y., Fu, H.: Flexible fusion network for multi-modal brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics 27(7), 3349–3359 (2023)
Rajendran, S., Rajagopal, S.K., Thanarajan, T., Shankar, K., Kumar, S., Alsub- aie, N.M., Ishak, M.K., Mostafa, S.M.: Automated segmentation of brain tumor mri images using deep learning. IEEE Access 11, 64758–64768 (2023)
Younis, A., Li, Q., Khalid, M., Clemence, B., Adamu, M.J.: Deep learning tech- niques for the classification of brain tumor: A comprehensive survey. IEEE Access 11, 113050–113063 (2023)
Metlek, S., C¸ etıner, H.: Resunet+: A new convolutional and attention block-based approach for brain tumor segmentation. IEEE Access (2023)
Farzamnia, A., Hazaveh, S.H., Siadat, S.S., Moung, E.G.: Mri brain tumor detec- tion methods using contourlet transform based on time adaptive self-organizing map. IEEE Access (2023)
Amara, K., Guerroudji, M.A., Kerdjidj, O., Zenati, N., Ramzan, N.: Holotu- mour: 6dof phantom head pose estimation based deep learning and brain tumour segmentation for ar visualisation and interaction. IEEE Sensors Journal (2023)
Shah, H.A., Saeed, F., Yun, S., Park, J.-H., Paul, A., Kang, J.-M.: A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientnet. Ieee Access 10, 65426–65438 (2022)
Ezhov, I., Mot, T., Shit, S., Lipkova, J., Paetzold, J.C., Kofler, F., Pellegrini, C., Kollovieh, M., Navarro, F., Li, H., et al.: Geometry-aware neural solver for fast bayesian calibration of brain tumor models. IEEE Transactions on Medical Imaging 41(5), 1269–1278 (2021)
Ahmad, S., Choudhury, P.K.: On the performance of deep transfer learning net- works for brain tumor detection using mr images. IEEE Access 10, 59099–59114 (2022)
Asif, S., Yi, W., Ain, Q.U., Hou, J., Yi, T., Si, J.: Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from mr images. IEEE Access 10, 34716–34730 (2022)
Hao, Q., Pei, Y., Zhou, R., Sun, B., Sun, J., Li, S., Kang, X.: Fusing multiple deep models for in vivo human brain hyperspectral image classification to identify glioblastoma tumor. IEEE Transactions on Instrumentation and Measurement 70, 1–14 (2021)
Kujur, A., Raza, Z., Khan, A.A., Wechtaisong, C.: Data complexity based eval- uation of the model dependence of brain mri images for classification of brain tumor and alzheimer’s disease. IEEE Access 10, 112117–112133 (2022)
Berker, Y., ElHarouni, D., Peterziel, H., Fiesel, P., Witt, O., Oehme, I., Schlesner, M., Oppermann, S.: Patient-by-patient deep transfer learning for drug-response profiling using confocal fluorescence microscopy of pediatric patient-derived tumor-cell spheroids. IEEE Transactions on Medical Imaging 41(12), 3981–3999 (2022)
Zhang, J.-W., Chen, W., Ly, K.I., Zhang, X., Yan, F., Jordan, J., Harris, G., Plotkin, S., Hao, P., Cai, W.: Dins: deep interactive networks for neurofibroma segmentation in neurofibromatosis type 1 on whole-body mri. IEEE journal of biomedical and health informatics 26(2), 786–797 (2021)
Liu, Y., Yin, M., Sun, S.: Detexnet: accurately diagnosing frequent and challeng- ing pediatric malignant tumors. IEEE Transactions on Medical Imaging 40(1), 395–404 (2020)
Gric, T., Sokolovski, S.G., Alekseev, A.G., Mamoshin, A.V., Dunaev, A., Rafailov, E.U.: The discrete analysis of the tissue biopsy images with metamaterial for- malization: identifying tumor locus. IEEE journal of selected topics in quantum electronics 27(5), 1–8 (2021)
Kromp, F., Fischer, L., Bozsaky, E., Ambros, I.M., D¨orr, W., Beiske, K., Ambros, P.F., Hanbury, A., Taschner-Mandl, S.: Evaluation of deep learning architectures for complex immunofluorescence nuclear image segmentation. IEEE Transactions on Medical Imaging 40(7), 1934–1949 (2021)
Wang, X., Liu, S., Yang, N., Chen, F., Ma, L., Ning, G., Zhang, H., Qiu, X., Liao, H.: A segmentation framework with unsupervised learning-based label mapper for the ventricular target of intracranial germ cell tumor. IEEE Journal of Biomedical and Health Informatics (2023)
Musa, M.J., Sharma, K., Cleary, K., Chen, Y.: Respiratory compensated robot for liver cancer treatment: Design, fabrication, and benchtop characterization. IEEE/ASME Transactions on Mechatronics 27(1), 268–279 (2021)
Saraf, R., Datta, A., Sima, C., Hua, J., Lopes, R., Bittner, M.L., Miller, T., Wilson-Robles, H.M.: In silico modeling of the induction of apoptosis by cryptotanshinone in osteosarcoma cell lines. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(3), 1683–1693 (2020)
Chen, T., Saadatnia, Z., Kim, J., Looi, T., Drake, J., Diller, E., Naguib, H.E.: Novel, flexible, and ultrathin pressure feedback sensor for miniaturized intraven- tricular neurosurgery robotic tools. IEEE Transactions on Industrial Electronics 68(5), 4415–4425 (2020)
Ling, Z., Yang, S., Gou, F., Dai, Z., Wu, J.: Intelligent assistant diagnosis system of osteosarcoma mri image based on transformer and convolution in developing countries. IEEE Journal of Biomedical and Health Informatics 26(11), 5563–5574 (2022)
Lim, A., Schonewille, A., Forbrigger, C., Looi, T., Drake, J., Diller, E.: Design and comparison of magnetically-actuated dexterous forceps instruments for neu- roendoscopy. IEEE Transactions on Biomedical Engineering 68(3), 846–856 (2020)
Gong, Y., Ye, D., Chien, C.-Y., Yue, Y., Chen, H.: Comparison of sonication pat- terns and microbubble administration strategies for focused ultrasound-mediated large-volume drug delivery. IEEE Transactions on Biomedical Engineering 69(11), 3449–3459 (2022)
Stenman, S., Bychkov, D., Ku¨cu¨kel, H., Linder, N., Haglund, C., Arola, J., Lundin, J.: Antibody supervised training of a deep learning based algorithm for leukocyte segmentation in papillary thyroid carcinoma. IEEE journal of biomedical and health informatics 25(2), 422–428 (2020)
Zhang, S., Miao, Y., Chen, J., Zhang, X., Han, L., Huang, Z., Pei, N., Liu, H., An, C.: Ccs-net: cascade detection network with the convolution kernel switch block and statistics optimal anchors block in hypopharyngeal cancer mri. IEEE journal of biomedical and health informatics 27(1), 433–444 (2022)
Gunderman, A.L., Musa, M., Gunderman, B.O., Banovac, F., Cleary, K., Yang, X., Chen, Y.: Autonomous respiratory motion compensated robot for ct-guided abdominal radiofrequency ablations. IEEE Transactions on Medical Robotics and Bionics 5(2), 206–217 (2023)
Gao, J., Gu, L., Min, X., Lin, P., Li, C., Zhang, Q., Rao, N.: Brain fingerprinting and lie detection: A study of dynamic functional connectivity patterns of decep- tion using eeg phase synchrony analysis. IEEE Journal of Biomedical and Health Informatics 26(2), 600–613 (2021)
Nakhla, S., Rahawy, A., Abd El Salam, M., Shalaby, T., Zaghloul, M., El-Abd, E.: Radiosensitizing and phototherapeutic effects of aunps are mediated by dif- ferential noxa and bim gene expression in mcf-7 breast cancer cell line. IEEE Transactions on NanoBioscience 20(1), 20–27 (2020)
Su, H., Kwok, K.-W., Cleary, K., Iordachita, I., Cavusoglu, M.C., Desai, J.P., Fischer, G.S.: State of the art and future opportunities in mri-guided robot- assisted surgery and interventions. Proceedings of the IEEE 110(7), 968–992 (2022)
Hameed, S.S., Hassan, W.H., Latiff, L.A., Muhammadsharif, F.F.: A compara- tive study of nature-inspired metaheuristic algorithms using a three-phase hybrid approach for gene selection and classification in high-dimensional cancer datasets. Soft Computing 25, 8683–8701 (2021)
Pang, Q., Zhang, L.: A recursive feature retention method for semi-supervised fea- ture selection. International Journal of Machine Learning and Cybernetics 12(9), 2639–2657 (2021)
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