Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm—Evaluation of Diagnostic Performance

Wuestemann, Jan; Hupfeld, S; Kupitz, Dennis; Genseke, Philipp; Schenke, Simone; Pech, Maciej; Kreißl, Michael C.; Großer, Oliver S.

doi:10.3390/cancers12092654

Cited by 21 publications

(11 citation statements)

References 37 publications

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“…However, even without conducting a RQS assessment of the identified deep radiomics studies, several clear methodological weaknesses exist among the reviewed studies. External validation was poor, with only a single study ( 131 ) conducting a validation of the model performance on an external test set. None of the papers were prospective in nature.…”

Section: Discussion and Future Recommendationsmentioning

confidence: 99%

“…With respect to imaging modalities, bone scintigraphy was the modality that was most commonly analysed (100,(125)(126)(127)(128)(129)(130)(131), and the salient characteristics of these papers are summarised in Table 5. Papandrianos and colleagues (128) designed a CNN architecture for bone metastases diagnosis, where patient bone scintigrams were classified into three classes: no metastasis, degenerative (defined as the absence of metastasis but the presence of degenerative lesions), and metastasis present.…”

Section: Deep Radiomicsmentioning

confidence: 99%

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Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

et al. 2021

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Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

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Section: Discussion and Future Recommendationsmentioning

confidence: 99%

Section: Deep Radiomicsmentioning

confidence: 99%

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

et al. 2021

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“…The U-Net algorithm is one of the most commonly used deep learning-based convolutional neural networks (CNNs) ( 13 ), which shows potential in detection, segmentation, and classification of metastatic lesions on MRI images such as brain metastases ( 14 , 15 ) and liver metastases ( 16 ). Concerning the automated bone metastasis analysis using the deep learning technique, the research trend is mainly on BS ( 17 , 18 ) and single-photon emission computerized tomography (SPECT) images ( 19 , 20 ); less attention has been paid to the diagnosis of mpMRI ( 21 , 22 ). To this end, we intend to apply the 3D U-Net ( 23 ) algorithm for the segmentation of bone metastases on mpMRI images.…”

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning

et al. 2021

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ObjectiveTo establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using diffusion-weighted imaging (DWI) and T1 weighted imaging (T1WI) images.MethodsThe model consisted of two 3D U-Net algorithms. A total of 859 patients with clinically suspected or confirmed PCa between January 2017 and December 2020 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. Then, 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from January to May 2021 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision, and F1-score were assessed at the lesion level.ResultsThe pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85, and the HD values were <15 mm. In the testing set, the AUC of the metastases detection at the patient level were 0.85 and 0.80 on DWI and T1WI images. At the lesion level, the F1-score achieved 87.6% and 87.8% concerning metastases detection on DWI and T1WI images, respectively. In the external dataset, the AUC of the model for M-staging was 0.94 and 0.89 on DWI and T1WI images.ConclusionThe deep learning-based 3D U-Net network yields accurate detection and segmentation of pelvic bone metastases for PCa patients on DWI and T1WI images, which lays a foundation for the whole-body skeletal metastases assessment.

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“…ANN-based models commonly have optimal accuracies and AUC values. Some evaluation indexes of ANN models even achieved an accuracy of 100%[ 81 , 179 ]. For further validation of ANN metrics, comparisons were also performed and can be divided into three aspects based on the compared objects.…”

Section: Features Limitations and Future Perspectivesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm—Evaluation of Diagnostic Performance

Cited by 21 publications

References 37 publications

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

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