Artificial intelligence – an aid for physicians in chordoma management? A systematic review of current applications
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Abstract
Introduction. Chordoma, a rare neoplasm originating from the notochord, poses diagnostic and therapeutic challenges due to its slow growth and complex anatomical locations. The advent of artificial intelligence (AI) offers promising avenues for improving chordoma management through enhanced diagnosis, prognostication, and treatment optimization.
Methods. This systematic review adhered to the PRISMA guidelines and focused on AI applications in chordoma management. A comprehensive literature search was conducted across seven major databases, and relevant data were extracted, including publication details, study aims, AI techniques employed, validation methods, and study results.
Results. AI techniques, including machine learning and deep learning, demonstrated efficacy in differentiating chordomas fromother neoplasms, segmenting tumor boundaries, predicting patient survival and recurrence, and guiding therapeutic strategies. Integration of radiomic features, clinical characteristics, and imagingmodalities facilitated accurate diagnosis and prognostication. Additionally, AI-driven approaches enabled drug repurposing and optimized treatment planning, particularly in radiation therapy.
Conclusions. The findings highlight the transformative potential of AI in revolutionizing chordoma management, offering personalized and precise approaches for diagnosis, prognostication, and therapeutic intervention. Collaborative efforts between clinicians, researchers, and technologists are essential to validate AI-driven algorithms and introduce them into clinical practice. Further research is warranted to address limitations and ensure the ethical deployment of AI technologies in healthcare to improve outcomes for chordoma patients.
Keywords: artificial intelligencemachine learningdeep learningLASSOSVMchordomaautomated tumor diagnosistumor survival prediction
References
- Kłosiński P, Lisiecki J, Goździewicz J, et al. Chordoma–leczenie i rokowanie. Współcz Onkol. 2003; 2: 107–114.
- Gulluoglu S, Turksoy O, Kuskucu A, et al. The molecular aspects of chordoma. Neurosurg Rev. 2016; 39(2): 185–96; discussion 196.
- Tenny S, Varacallo M. Chordoma. In: StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL) 2022.
- George B, Bresson D, Herman P, et al. Chordomas: A Review. Neurosurg Clin N Am. 2015; 26(3): 437–452.
- Young VA, Curtis KM, Temple HT, et al. Characteristics and Patterns of Metastatic Disease from Chordoma. Sarcoma. 2015; 2015: 517657.
- Heery CR. Erratum to: Chordoma: The Quest for Better Treatment Options. Oncol Ther. 2016; 4(1): 53–55.
- Li Y, Liu Y, Liang Y, et al. Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol. 2022; 32(11): 8039–8051.
- Hassan C, Spadaccini M, Iannone A, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021; 93(1): 77–85.e6.
- Bedrikovetski S, Dudi-Venkata NN, Kroon HM, et al. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer. 2021; 21(1): 1058.
- Page M, McKenzie J, Bossuyt P, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021: n71.
- Boussioux L, Ma Yu, Thomas NK, et al. Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble. Int J Radiat Oncol Biol Phys. 2023; 117(3): 738–749.
- Li L, Wang Ke, Ma X, et al. Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma. Eur J Radiol. 2019; 118: 81–87.
- Song L, Hua H, Li F, et al. Anatomical Partition‐Based Deep Learning: An Automatic Nasopharyngeal MRI Recognition Scheme. J Magn Reson Imaging. 2022; 56(4): 1220–1229.
- Liu R, Pan D, Xu Y, et al. A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors. Eur Radiol. 2022; 32(2): 1371–1383.
- Nie P, Zhao X, Wang N, et al. A Computed Tomography Radiomics Nomogram in Differentiating Chordoma From Giant Cell Tumor in the Axial Skeleton. J Comput Assist Tomogr. 2023; 47(3): 453–459.
- Sun W, Liu S, Guo J, et al. A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours. Cancer Imaging. 2021; 21(1): 20.
- Yin P, Mao N, Zhao C, et al. A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI. J Magn Reson Imaging. 2019; 49(3): 752–759.
- Yin P, Mao N, Wang S, et al. Clinical-radiomics nomograms for pre-operative differentiation of sacral chordoma and sacral giant cell tumor based on 3D computed tomography and multiparametric magnetic resonance imaging. Br J Radiol. 2019; 92(1101): 20190155.
- Yin P, Zhi X, Sun C, et al. Radiomics Models for the Preoperative Prediction of Pelvic and Sacral Tumor Types: A Single-Center Retrospective Study of 795 Cases. Front Oncol. 2021; 11: 709659.
- Yin P, Mao N, Zhao C, et al. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol. 2019; 29(4): 1841–1847.
- Yin P, Mao N, Chen H, et al. Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors. Front Oncol. 2020; 10: 564725.
- Yamazawa E, Takahashi S, Shin M, et al. MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study. Cancers (Basel). 2022; 14(13).
- Herrgott GA, Asmaro KP, Wells M, et al. Detection of tumor-specific DNA methylation markers in the blood of patients with pituitary neuroendocrine tumors. Neuro Oncol. 2022; 24(7): 1126–1139.
- Zuccato JA, Patil V, Mansouri S, et al. DNA methylation-based prognostic subtypes of chordoma tumors in tissue and plasma. Neuro Oncol. 2022; 24(3): 442–454.
- Buizza G, Paganelli C, D'Ippolito E, et al. Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma. Cancers (Basel). 2021; 13(2).
- Cheng D, Liu D, Li X, et al. Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma. World Neurosurg. 2023; 178: e835–e845.
- Cheng P, Xie X, Knoedler S, et al. Predicting overall survival in chordoma patients using machine learning models: a web-app application. J Orthop Surg Res. 2023; 18(1): 652.
- Karhade AV, Thio Q, Ogink P, et al. Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival. World Neurosurg. 2018; 119: e842–e847.
- Morelli L, Parrella G, Molinelli S, et al. A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy. Cancers (Basel). 2022; 15(1).
- Wei W, Wang Ke, Liu Z, et al. Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma. Radiother Oncol. 2019; 141: 239–246.
- Zhai Y, Bai J, Xue Y, et al. Development and validation of a preoperative MRI-based radiomics nomogram to predict progression-free survival in patients with clival chordomas. Front Oncol. 2022; 12: 996262.
- Zou MX, Pan Y, Huang W, et al. A four-factor immune risk score signature predicts the clinical outcome of patients with spinal chordoma. Clin Transl Med. 2020; 10(1): 224–237.
- Ghaith AK, Akinduro OO, Alexander AY, et al. Immunohistochemical markers predicting long-term recurrence following clival and spinal chordoma resection: a multicenter study. Neurosurg Focus. 2023; 54(6): E15.
- Dinkla AM, Wolterink JM, Maspero M, et al. MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network. Int J Radiat Oncol Biol Phys. 2018; 102(4): 801–812.
- Anderson E, Havener TM, Zorn KM, et al. Synergistic drug combinations and machine learning for drug repurposing in chordoma. Sci Rep. 2020; 10(1): 12982.