Artificial intelligence in musculoskeletal oncological radiology

Vogrin, Matjaž; Trojner, Teodor; Kelc, Robi

doi:10.2478/raon-2020-0068

Cited by 15 publications

(14 citation statements)

References 51 publications

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“…The focus of AI-related research for bone tumor diagnosis is mainly on the radiographic analysis for the moment (27,28). Bao et al (29) have incorporated various features from radiographic observations and demographic information to build a naïve Bayesian-based model for ranking and classifying a wide range of bone tumor diagnoses.…”

Section: Discussionmentioning

confidence: 99%

Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

et al. 2021

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BackgroundHistopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists.MethodsA total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four pathologists with different experiences was compared to the best-performing models. The gradient-weighted class activation mapping was used to visualize patch’s important area.ResultsVGG-16 and Inception V3 performed better than other models in patch-level binary and ternary classification. For slide-level prediction, VGG-16 and Inception V3 had area under curve of 0.962 and 0.971 for binary classification and Cohen’s kappa score (CKS) of 0.731 and 0.802 for ternary classification. The senior pathologist had CKS of 0.685 comparable to both models (p = 0.688 and p = 0.287) while attending and junior pathologists showed lower CKS than the best model (each p < 0.05). Visualization showed that the DL model depended on pathological features to make predictions.ConclusionDL can effectively classify bone tumors histopathologically in terms of aggressiveness with performance similar to senior pathologists. Our results are promising and would help expedite the future application of DL-assisted histopathological diagnosis for bone tumors.

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Section: Discussionmentioning

confidence: 99%

Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

et al. 2021

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“…In recent years, the advent of artificial intelligence (AI) applications in medical imaging has also impacted musculoskeletal oncological imaging [ 19 ]. AI decision support systems promise to reduce interobserver variability in reporting.…”

Section: Discussionmentioning

confidence: 99%

The Lodwick classification for grading growth rate of lytic bone tumors: a decision tree approach

2021

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The estimation of growth rate of lytic bone tumors based on conventional radiography has been extensively studied. While benign tumors exhibit slow growth, malignant tumors are more likely to show fast growth. The most frequently used algorithm for grading of growth rate on conventional radiography was published by Gwilym Lodwick. Based on the evaluation of the four descriptors (1) type of bone destruction (including the subdescriptor “margin” for geographic lesions), (2) penetration of cortex, (3) presence of a sclerotic rim, and (4) expanded shell, an overall growth grade (IA, IB, IC, II, III) can be assigned, with higher grade representing faster tumor growth. In this article, we provide an easy-to-use decision tree of Lodwick’s original grading algorithm, suitable for teaching of students and residents. Subtleties of the grading algorithm and potential pitfalls in clinical practice are explained and illustrated. Exemplary conventional radiographs provided for each descriptor in the decision tree may be used as a guide and atlas for assisting in evaluation of individual features in daily clinical practice.

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“…One duplicate was discarded and 38 articles subsequently met the eligibility criteria (Fig. 1) [8,10,. 2 and 3.…”

Section: Selection and Methodological Characteristicsmentioning

confidence: 99%

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

et al. 2022

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Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. Key Points • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

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Artificial intelligence in musculoskeletal oncological radiology

Cited by 15 publications

References 51 publications

Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

The Lodwick classification for grading growth rate of lytic bone tumors: a decision tree approach

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

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