Deep learning–based diagnosis of osteoblastic bone metastases and bone islands in computed tomograph images: a multicenter diagnostic study

Xiong, Yuchao; Guo, Wei; Liang, Zhanhua; Wu, Li; Ye, Guoxi; Liang, Yingying; Wen, Chao; Yang, Feng; Chen, Song; Zeng, Xuwen; Xu, Fan

doi:10.1007/s00330-023-09573-5

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Hence, in this study, we employed a DTL model constructed using 2.5D segmentation to assess the aggressiveness of PCa. Our approach is akin to the 2.5D network described in prior literature ( 32 , 34 ), which selects the largest lesion layer as the central layer and utilizes the upper and lower layers as input data. This segmentation strategy mirrors that of many other organs, such as the brain ( 35 , 36 ), liver ( 37 ), pancreas ( 38 ), and kidney ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI)

Li,

Ding,

Yin

et al. 2024

Quant Imaging Med Surg

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Background The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI). Methods A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves. Results DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D. Conclusions A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.

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Section: Discussionmentioning

confidence: 99%

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI)

Li,

Ding,

Yin

et al. 2024

Quant Imaging Med Surg

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Development of a Dual‐Plane MRI‐Based Deep Learning Model to Assess the 1‐Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy

Wang,

Lin,

Liao

et al. 2024

Magnetic Resonance Imaging

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BackgroundTubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explored.Purpose/HypothesisTo evaluate whether integrating preoperative dual‐plane MRI‐based DL features with clinical features can assess 1‐year outcomes in TMD for LDH.Study TypeRetrospective.Population/SubjectsThe study involved 548 patients who underwent TMD between January 2016 and January 2021. Training set (N = 305, mean age 51.85 ± 13.84 years, 56.4% male). Internal validation set (N = 131, mean age 51.85 ± 13.84 years, 54.2% male). External validation set (N = 112, mean age 51.54 ± 14.43 years, 50.9% male).Field Strength/Sequence3 T MRI with sagittal and transverse T2‐weighted sequences (Fast Spin Echo).AssessmentGround truth labels were based on improvement rate in 1‐year Japanese Orthopaedic Association (JOA) scores. Information on 42 preoperative clinical features was collected. The largest protrusions were identified from T2 MRI by three clinicians and were used to train deep learning models (ResNet50, ResNet101, and ResNet152) to extract DL features. After feature selection, three models were built, namely, clinical, DL, and combined models.Statistical TestsChi‐square or Fisher's exact tests was used for group comparisons. Quantitative differences were analyzed using the t‐test or Mann–Whitney U test. P‐values <0.05 were considered significant. Models were validated on internal and external datasets using metrics such as the area under the curve (AUC).ResultsThe AUCs of the clinical models achieved 0.806 (internal) and 0.779 (external). ResNet152 performed best in three DL models, with AUCs of 0.858 (internal) and 0.834 (external). The combined model achieved AUCs of 0.889 (internal) and 0.857 (external).Data ConclusionA model combining preoperative dual‐plane MRI DL features and clinical features can assess 1‐year outcomes of TMD for LDH.Evidence Level4Technical EfficacyStage 2

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Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs

Tassoker,

Öziç,

Yuce

2024

Sci Rep

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Idiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure.

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Deep learning–based diagnosis of osteoblastic bone metastases and bone islands in computed tomograph images: a multicenter diagnostic study

Cited by 3 publications

References 28 publications

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI)

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI)

Development of a Dual‐Plane MRI‐Based Deep Learning Model to Assess the 1‐Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy

Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs

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