Abstract
Early and accurate diagnosis of osteosarcomas (OS) is of great clinical significance, and machine learning (ML) based methods are increasingly adopted. However, current ML-based methods for osteosarcoma diagnosis consider only X-ray images, usually fail to generalize to new cases, and lack explainability. In this paper, we seek to explore the capability of deep learning models in diagnosing primary OS, with higher accuracy, explainability, and generality. Concretely, we analyze the added value of integrating the biochemical data, i.e., alkaline phosphatase (ALP) and lactate dehydrogenase (LDH), and design a model that incorporates the numerical features of ALP and LDH and the visual features of X-ray imaging through a late fusion approach in the feature space. We evaluate this model on real-world clinic data with 848 patients aged from 4 to 81. The experimental results reveal the effectiveness of incorporating ALP and LDH simultaneously in a late fusion approach, with the accuracy of the considered 2608 cases increased to 97.17%, compared to 94.35% in the baseline. Grad-CAM visualizations consistent with orthopedic specialists further justified the model’s explainability.
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The datasets generated and/or analyzed during the current study are not publicly available due to privacy restrictions and ethical considerations related to patient data.
References
Gaume M, Chevret S, Campagna R, Larousserie F, Biau D. The appropriate and sequential value of standard radiograph, computed tomography and magnetic resonance imaging to characterize a bone tumor. Sci Rep. 2022;12:6196.
Shao R, et al. Bone tumors effective therapy through functionalized hydrogels: Current developments and future expectations. Drug Delivery. 2022;29:1631–47.
Mullard M, et al. Sonic hedgehog signature in pediatric primary bone tumors: effects of the gli antagonist gant61 on ewing’s sarcoma tumor growth. Cancers. 2020;12:3438.
Fauske L, Bruland OS, Grov EK, Bondevik H, et al. Cured of primary bone cancer, but at what cost: a qualitative study of functional impairment and lost opportunities. Sarcoma. 2015;2015: 484196.
Davies M, Lalam R, Woertler K, Bloem JL, Åström G. Ten commandments for the diagnosis of bone tumors. Semin Musculoskelet Radiol. 2020;24(3):203–13.
Stefanini FS, Gois FWC, de Arruda TCSB, Bitencourt AGV, Cerqueira WS. Primary bone lymphoma: pictorial essay. Radiol Bras. 2020;53:419–23.
Miwa S, Otsuka T. Practical use of imaging technique for management of bone and soft tissue tumors. J Orthop Sci. 2017;22:391–400.
Lindsey BA, Markel JE, Kleinerman ES. Osteosarcoma overview. Rheumatol Ther. 2017;4:25–43.
Richens JG, Lee CM, Johri S. Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun. 2020;11:3923.
Lei Y, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process. 2020;138: 106587.
Shung DL, Sung JJ. Challenges of developing artificial intelligence-assisted tools for clinical medicine. J Gastroenterol Hepatol. 2021;36:295–8.
Tătaru OS, et al. Artificial intelligence and machine learning in prostate cancer patient management—current trends and future perspectives. Diagnostics. 2021;11:354.
He F, et al. Study on machine learning model of primary bone tumor around knee joint assisted diagnosis based on X-ray images. Prog Mod Biomed. 2021;21 (in Chinese)
Olczak J, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88:581–6.
Fave X, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017;7:588.
Alzubaidi L, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8:1–74.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv Preprint (2015). arXiv:1512.03385
Xu W, Fu Y-L, Zhu D. ResNet and its application to medical image processing: research progress and challenges. Comput Methods Programs Biomed. 2023;240: 107660.
Soni M, et al. Hybridizing convolutional neural network for classification of lung diseases. Int J Swarm Intell Res (IJSIR). 2022;13:1–15.
Chowdhury NK, Rahman MM, Kabir MA. PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images. Health Inf Sci Syst. 2020;8:27.
Zhang Y, Tiňo P, Leonardis A, Tang K. A survey on neural network interpretability. IEEE Trans Emerg Top Comput Intell. 2021;5:726–42.
Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl. 2020;32:18069–83.
Selvaraju RR, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. arXiv e-prints (2016). arXiv:1610.02391
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. arXiv e-prints (2015). arXiv:1512.04150
Chan L, Hosseini MS, Plataniotis KN. A comprehensive analysis of weakly-supervised semantic segmentation in different image domains. Int J Comput Vis. 2021;129:361–84.
Zhang X, et al. Prospective clinical research of radiomics and deep learning in oncology: a translational review. Crit Rev Oncol Hematol. 2022;179: 103823.
Sarki R, Ahmed K, Wang H, Zhang Y. Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst. 2020;8:32.
Abd El-Wahab BS, Nasr ME, Khamis S, Ashour AS. Btc-fcnn: Fast convolution neural network for multi-class brain tumor classification. Health Inf Sci Syst. 2023;11:3.
Bansal P, Gehlot K, Singhal A, Gupta A. Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm. Multimedia Tools Appl. 2022;81:8807–34.
Zhao Y, et al. Identification of gastric cancer with convolutional neural networks: a systematic review. Multimedia Tools Appl. 2022;81:11717–36.
Bhandari B, Alsadoon A, Prasad P, Abdullah S, Haddad S. Deep learning neural network for texture feature extraction in oral cancer: Enhanced loss function. Multimedia Tools Appl. 2020;79:27867–90.
Anoop V, Bipin PR, Anoop BK. Automated biomedical image classification using multi-scale dense dilated semi-supervised u-net with cnn architecture. Multimed Tools Appl. 2024;83:30641–73. https://doi.org/10.1007/s11042-023-16659-1.
Bai Q, Su C, Tang W, Li Y. Machine learning to predict end stage kidney disease in chronic kidney disease. Sci Rep. 2022;12:8377.
Anand D, Arulselvi G, Balaji G, Chandra GR. A deep convolutional extreme machine learning classification method to detect bone cancer from histopathological images. Int J Intell Syst Appl Eng. 2022;10:39–47.
von Schacky CE, et al. Development and evaluation of machine learning models based on x-ray radiomics for the classification and differentiation of malignant and benign bone tumors. Eur Radiol. 2022;32:6247–57.
Liu R, et al. A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors. Eur Radiol. 2022;32:1371–83.
Cole S, Gianferante DM, Zhu B, Mirabello L. Osteosarcoma: a surveillance, epidemiology, and end results program-based analysis from 1975 to 2017. Cancer. 2022;128:2107–18.
Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385:2066–76.
Bian J, et al. Research progress in the mechanism and treatment of osteosarcoma. Chin Med J. 2023;136:2412–20.
Gorlick R, et al. Children’s oncology group’s 2013 blueprint for research: bone tumors. Pediatr Blood Cancer. 2013;60:1009–15.
Gu R, Sun Y. Does serum alkaline phosphatase level really indicate the prognosis in patients with osteosarcoma? a meta-analysis. J Cancer Res Ther. 2018;14:S468–72.
Su Z, Huang F, Yin C, Yu Y, Yu C. Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction. J Orthop Surg. 2023;31:10225536231177102.
Basoli S, et al. The prognostic value of serum biomarkers for survival of children with osteosarcoma of the extremities. Curr Oncol. 2023;30:7043–54.
Fu Y, Lan T, Cai H, Lu A, Yu W. Meta-analysis of serum lactate dehydrogenase and prognosis for osteosarcoma. Medicine. 2018;97: e0741.
Biermann JS, et al. NCCN guidelines insights: bone cancer, version 2.2017. J Natl Compreh Cancer Netw. 2017;15(2):155–67. https://doi.org/10.6004/jnccn.2017.0017.
Ottaviani G, Jaffe N. The epidemiology of osteosarcoma. Cancer Treat Res. 2010;2:3–13.
Sadykova LR, et al. Epidemiology and risk factors of osteosarcoma. Cancer Invest. 2020;38:259–69.
Acknowledgements
This work is supported by the Natural Science Foundation of Jiangsu Province under grant BK20230083, Xicheng district financial science and technology special project XCSTS-SD2022-15, Peking University People’s Hospital research and development funds RDX2023-01.
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Wang, S., Shen, Y., Zeng, F. et al. Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning. Health Inf Sci Syst 12, 31 (2024). https://doi.org/10.1007/s13755-024-00288-5
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DOI: https://doi.org/10.1007/s13755-024-00288-5