Classification and Prediction of Pleuro Pulmonary Blastoma Using Deep Learning Models

  • Authors

    • Arigela Jyothi Department of Computer Science and Engineering, Keshav Memorial Institute of Technology (KMIT), Narayanguda, Hyderabad, Telangana, India
    • Janapareddy Uttara Alekhya Department of Advanced Computer Science and Engineering, Vignan's Institute of Information Technology (A), Visakhapatnam, Andhra Pradesh, India
    • Anuradha Patnala Department of Computer Science and Engineering, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India
    • Mosuri Santhosh Kumar Department of Electrical and Electronics Engineering, GMR Institute of Technology (A), Rajam, Andhra Pradesh, India
    • Gorli Ramya Department of Computer Science and Engineering, Vignan's Institute of Engineering for Women (A), Visakhapatnam, Andhra Pradesh, India
    • Rajendra Kumar Ganiya Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
    https://doi.org/10.14419/gz68s920

    Received date: June 29, 2025

    Accepted date: September 10, 2025

    Published date: September 17, 2025

  • Pleuro Pulmonary Blastoma, Machine Learning, Classification, ResNet-based Convolutional Neural Network, Long Short-Term Memory

  • Abstract

    Pleuro-pulmonary Blastoma (PPB) is an uncommon childhood illness caused by embryonic malignancies that have aberrant tissue growth on the pleural surfaces and in the lung parenchyma. Tumors can develop by several methods, allowing them to be categorized into categories I, II, and III. Each type is associated with distinct variances in the age at which they are diagnosed and the prognosis. Our goal was to provide a comprehensive examination of the relevant literature, outlining the features of these tumors and the use of multidisciplinary approaches to treat them, with a specific emphasis on surgical intervention. As described in prior writings, pathologists use patient samples such as MRI scan images, DNA sequencing, etc., to predict and categorize PPB variations. Various machine learning methods are utilized to extract features from proteins according to their protein family, but in the suggested methodology, we used various deep learning models to automatically extract features and for classification. We considered using Deep Learning algorithms, such as ResNet-based Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), by taking into consideration protein sequence as input data to obtain accurate results and perform the prognosis of type I, II, and III. We discover the connection between the functional annotations of unaligned amino acid sequences and these deep learning models.

  • References

    1. Bauer, A. J., Stewart, D. R., Kamihara, J., Harris, A. K., Turner, J., Shah, R., Schneider, K. W., Schneider, K., Carr, A. G., Harney, L. A., Frazier, A. L., Orbach, D., Schneider, D. T., Malkin, D., Dehner, L. P., Messinger, Y. H., Hill, D. A., & Schultz, K. A. P, DICER1 and Associated Condi-tions: Identification of At-risk Individuals and Recommended Surveillance Strategies-Response. Clinical cancer research : an official journal of the American Association for Cancer Research, 25(5), (2019) 1689–1690. https://doi.org/10.1158/1078-0432.CCR-18-3495
    2. Stewart, D. R., Best, A. F., Williams, G. M., Harney, L. A., Carr, A. G., Harris, A. K., Kratz, C. P., Dehner, L. P., Messinger, Y. H., Rosenberg, P. S., Hill, D. A., & Schultz, K. A. P, Neoplasm Risk Among Individuals With a Pathogenic Germline Variant in DICER1. Journal of clinical oncolo-gy : official journal of the American Society of Clinical Oncology, 37(8), (2019) 668–676. https://doi.org/10.1200/JCO.2018.78.4678.
    3. Brenneman, M., Field, A., Yang, J., Williams, G., Doros, L., Rossi, C., Schultz, K. A., Rosenberg, A., Ivanovich, J., Turner, J., Gordish-Dressman, H., Stewart, D., Yu, W., Harris, A., Schoettler, P., Goodfellow, P., Dehner, L., Messinger, Y., & Hill, D. A, Temporal order of RNase IIIb and loss-of-function mutations during development determines phenotype in pleuropulmonary blastoma / DICER1 syndrome: a unique variant of the two-hit tumor suppression model. F1000Research, 4, (2015) 214. https://doi.org/10.12688/f1000research.6746.2
    4. Messinger, Y. H., Stewart, D. R., Priest, J. R., Williams, G. M., Harris, A. K., Schultz, K. A., Yang, J., Doros, L., Rosenberg, P. S., Hill, D. A., & Dehner, L. P, Pleuropulmonary blastoma: a report on 350 central pathology-confirmed pleuropulmonary blastoma cases by the International Pleuro-pulmonary Blastoma Registry. Cancer, 121(2), (2015) 276–285. https://doi.org/10.1002/cncr.29032
    5. Hill, D. A., Jarzembowski, J. A., Priest, J. R., Williams, G., Schoettler, P., & Dehner, L. P, Type I pleuropulmonary blastoma: pathology and biolo-gy study of 51 cases from the international pleuropulmonary blastoma registry. The American journal of surgical pathology, 32(2), (2008) 282–295. https://doi.org/10.1097/PAS.0b013e3181484165
    6. López-Andreu, J. A., Ferrís-Tortajada, J., & Gómez, J, Pleuropulmonary blastoma and congenital cystic malformations. The Journal of pediatrics, 129(5), (1996) 773–775. https://doi.org/10.1016/s0022-3476(96)70176-2
    7. Sun, Y., Zhu, S., Ma, K., Liu, W., Yue, Y., Hu, G., Lu, H., & Chen, W, Identification of 12 cancer types through genome deep learning. Scientific reports, 9(1), (2019) 17256. https://doi.org/10.1038/s41598-019-53989-3
    8. Song, Y, CT Radio Genomics of Non-Small Cell Lung Cancer Using Machine and Deep Learning. 2021 IEEE International Conference on Con-sumer Electronics and Computer Engineering (ICCECE), (2019) 128-139. https://doi.org/10.1109/ICCECE51280.2021.9342170.
    9. Adetiba, E., & Olugbara, O. O, Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features. The-ScientificWorldJournal, 2015, (2015) 786013. https://doi.org/10.1155/2015/786013
    10. Zhang, B., Qi, S., Monkam, P., Li, C., Yang, F., Yao, Y., & Qian, W, Ensemble learners of multiple deep CNNs for pulmonary nodules Classifica-tion using CT images. IEEE Access, 7, (2019) 110358–110371. https://doi.org/10.1109/access.2019.2933670.
    11. Shaikh, F., & Rao, D, Prediction of Cancer Disease using Machine learning Approach. Materials Today Proceedings, 50, (2022) 40–47. https://doi.org/10.1016/j.matpr.2021.03.625
    12. Gunasekaran, H., Ramalakshmi, K., Rex Macedo Arokiaraj, A., Deepa Kanmani, S., Venkatesan, C., & Suresh Gnana Dhas, C, Analysis of DNA Sequence Classification Using CNN and Hybrid Models. Computational and mathematical methods in medicine, 2021, (2021) 1835056. https://doi.org/10.1155/2021/1835056
    13. Hu, H., Li, Z., Elofsson, A., & Xie, S, A Bi-LSTM based ensemble algorithm for prediction of protein secondary structure. Applied Sciences, 9(17), (2019) 3538. https://doi.org/10.3390/app9173538
    14. Sharma, B. S., Prabhakaran, V., Desai, A. P., Bajpai, J., Verma, R. J., & Swain, P. K, Post-translational Modifications (PTMs), from a Cancer Per-spective: An Overview. Oncogen, 2(3), (2019). https://doi.org/10.35702/onc.10012
    15. Kao, H., Nguyen, V., Huang, K., Chang, W., & Lee, T, SuccSite: Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites. Genomics Proteomics & Bioinformatics, 18(2), (2020) 208–219. https://doi.org/10.1016/j.gpb.2018.10.010
    16. Momenzadeh, M., Sehhati, M., & Rabbani, H, Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profiles. Journal of Biomedical Informatics, 111, (2020) 103570. https://doi.org/10.1016/j.jbi.2020.103570
    17. Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R. H., Saigo, H., & Kc, D. B, DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinformatics, 21(S3), (2020). https://doi.org/10.1186/s12859-020-3342-z
    18. Pandey, A., & Roy, S. S, Protein sequence classification using convolutional neural network and natural language processing. In Studies in big data, (2022) 133–144. https://doi.org/10.1007/978-981-16-9158-4_9
    19. C. Sekhar, K. Pavani and M. S. Rao, "Comparative analysis on Intrusion Detection system through ML and DL Techniques: Survey," 2021 Interna-tional Conference on Computational Intelligence and Computing Applications (ICCICA), Nagpur, India, 2021, pp. 1-5, doi: 10.1109/ICCICA52458.2021.9697291.
    20. Karagöz, M. A., & Nalbantoglu, O. U, Taxonomic classification of metagenomic sequences from Relative Abundance Index profiles using deep learning. Biomedical Signal Processing and Control, 67, (2021) 102539. https://doi.org/10.1016/j.bspc.2021.102539
    21. Deorowicz S, FQSqueezer: k-mer-based compression of sequencing data. Scientific reports, 10(1), (2020) 578. https://doi.org/10.1038/s41598-020-57452-6
    22. Roy, S. S., Sikaria, R., & Susan, A, A deep learning based CNN approach on MRI for Alzheimer’s disease detection. Intelligent Decision Technol-ogies, 13(4), (2020) 495–505. https://doi.org/10.3233/idt-190005
    23. Yu, L., Tanwar, D. K., Penha, E. D. S., Wolf, Y. I., Koonin, E. V., & Basu, M. K, Grammar of protein domain architectures. Proceedings of the National Academy of Sciences, 116(9), (2019) 3636–3645. https://doi.org/10.1073/pnas.1814684116
    24. Roy, S. S., & Taguchi, Y. H,. Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decom-position based unsupervised feature extraction. Scientific reports, 11(1), (2021) 8909. https://doi.org/10.1038/s41598-021-87779-7
    25. Balas, V. E., Roy, S. S., Sharma, D., & Samui, P, Handbook of Deep Learning Applications. Smart innovation, systems and technologies. (2019). https://doi.org/10.1007/978-3-030-11479-4
    26. A. M. Remita and A. B. Diallo, Statistical linear models in virus genomic alignment-free classification: application to hepatitis C viruses, in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, November 2019, (2019) 474-481, https://doi.org/10.1109/BIBM47256.2019.898337
    27. Zhang, D., & Kabuka, M. R, Protein Family Classification from Scratch: A CNN Based Deep Learning Approach. IEEE/ACM transactions on computational biology and bioinformatics, 18(5), (2021) 1996–2007. https://doi.org/10.1109/TCBB.2020.2966633
    28. Cheng, J., Liu, Y., & Ma, Y, Protein secondary structure prediction based on integration of CNN and LSTM model. Journal of Visual Communica-tion and Image Representation, 71, (2020) 102844. https://doi.org/10.1016/j.jvcir.2020.102844
    29. Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., . . . Colwell, L. J, Using deep learning to annotate the protein universe. Nature Biotechnology, 40(6), (2022) 932–937. https://doi.org/10.1038/s41587-021-01179-w
    30. Sabapathy, D. G., Guillerman, R. P., Orth, R. C., Zhang, W., Messinger, Y., Foulkes, W., Priest, J. R., & Annapragada, A. V, Radiographic screen-ing of infants and young children with genetic predisposition for rare malignancies: DICER1 mutations and pleuropulmonary blastoma. AJR. American journal of roentgenology, 204(4), (2015) W475–W482. https://doi.org/10.2214/AJR.14.12802
    31. Schultz, K. A., Harris, A., Williams, G. M., Baldinger, S., Doros, L., Valusek, P., Frazier, A. L., Dehner, L. P., Messinger, Y., & Hill, D. A, Judi-cious DICER1 testing and surveillance imaging facilitates early diagnosis and cure of pleuropulmonary blastoma. Pediatric blood & cancer, 61(9), (2014) 1695–1697. https://doi.org/10.1002/pbc.25092

  • Downloads

  • How to Cite

    Jyothi , A. ., Alekhya, J. U. . ., Patnala , A. ., Kumar , M. S. ., Ramya , G. ., & Ganiya , R. K. . (2025). Classification and Prediction of Pleuro Pulmonary Blastoma Using Deep Learning Models. International Journal of Basic and Applied Sciences, 14(5), 617-624. https://doi.org/10.14419/gz68s920