Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms

Georgeanu, Vlad Alexandru; Mămuleanu, Mădălin; Ghiea, Sorin; Selişteanu, Dan

doi:10.3390/medicina58050636

Cited by 21 publications

(12 citation statements)

References 34 publications

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“…These can be used singularly or in a combined manner in order to obtain a more robust system. In the work [ 34 ], an MRI-based DL analysis is performed in order to predict the malignancy of bone tumor samples, exploiting two pre-trained ResNet50 architectures. Further patient demographics information is then combined together with the network outputs and given to a fully connected layer to obtain the final prediction.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Conforti

D’Acunto

Russo

2022

Sensors

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The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Conforti

D’Acunto

Russo

2022

Sensors

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“…Machine learning techniques using radiomic features on MRI have been shown to have high performance for predicting benign vs. malignant bone lesions. Using pre-trained ResNet50 image classifiers, Georgeanu et al [ 93 ] were capable of predicting the malignant potential of bone tumours in 93.7% of cases using T1-weighted sequences and 86.7% using T2-weighted sequences. A model developed by Chianca et al [ 95 ] for classifying vertebral lesions into benign vs. malignant (primary malignant and metastatic lesions) demonstrated 94.0% accuracy in the internal test dataset and 86% accuracy in an external validation dataset.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Ong

Zhu

Tan

et al. 2023

Cancers

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An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

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“…d 1 is the first map from the error transmission, E is the error matrix, and extra maps from the error transmission and its error calculation are mathematically represented in Equations ( 8)- (10).…”

Section: Tumor Features Extraction Using Convolutional Histogram Of O...mentioning

confidence: 99%

“…To identify cancerous bone tumors, Georgeanu et al 10 established DL approaches. The main objective of this article is to accurately detect malignancies from MRI images.…”

Section: Related Workmentioning

confidence: 99%

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

Vijayaraj,

Abirami,

Mohanty

et al. 2023

Int J Imaging Syst Tech

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In our human body bones are the most significant part, which helps people to move and perform several activities. But, the cancer is caused by producing abnormal cell, which is rapidly spread to the whole parts of the body. Bone cancer is one of the critical types due to its malignancy more than other cancers. The approach involves preprocessing and segmentation of input images to remove noise and resize images, followed by feature extraction using a Convolutional histogram of oriented gradients (ConvHisOrGrad). The ROI extraction helps to accurately identify abnormal parts around the cancerous area. The Extreme Deep Convolutional learning machine (Ex‐ConVLM) is used for normal and cancerous bone classification based on the texture properties of bone MRI images. The proposed technique was evaluated using a dataset of 220 bone MRIs for tumor classes classified as necrotic, non‐tumor, and viable‐tumor. Results showed that the proposed technique outperformed existing techniques with the highest accuracy of 97% for the necrotic tumor class, 98.2% for the non‐tumor class, and 98.6% for the viable tumor class. The fine‐tuned model shows promising performance in detecting malignancy in bone based on histological images. In summary, the proposed technique utilizes deep learning architectures and ROI extraction for the accurate identification of abnormal parts in bone MRI images, achieving state‐of‐the‐art performance in the detection and categorization of bone cancers.

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Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms

Cited by 21 publications

References 34 publications

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

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