An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images

Zhu, Lianghui; Shi, Huijuan; Wei, Huiting; Wang, Chengjiang; Shu, Shi; Zhang, Fenfen; Yan, Renao; Liu, Yiqing; He, Tingting; Wang, Liyuan; Cheng, Junru; Duan, Hufei; Du, Hong; Meng, Fengjiao; Zhao, Wei; Gu, Xia; Guo, Linlang; Ni, Yingpeng; He, Yonghong; Guan, Tian; Han, Anjia

doi:10.1016/j.ebiom.2022.104426

Cited by 7 publications

(5 citation statements)

References 43 publications

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“…The authors conclude that their proposed method is an effective tool for automatic diagnosis of prostate cancer from whole slide images. Zhu et al [ 29 ] present a DL approach to accurately predict the origin of bone metastatic cancer using digital pathological images. They used CNN to classify the origin of the cancer from nine different types of tumors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additional to, Zhu et al [ 29 ] suggest a model to predict the origin of bone metastatic cancer using DL architecture on digital pathological images, the limitations in the dataset, the focus on bone metastatic cancer only, the lack of detailed explanation of the features used, the absence of comparison with other models, and the potential limitations and biases of using DL architecture in medical image analysis need to be taken into consideration.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

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Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Talaat,

El-Sappagh,

Alnowaiser

et al. 2024

BMC Med Inform Decis Mak

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Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Implementation and Evaluationmentioning

confidence: 99%

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Talaat,

El-Sappagh,

Alnowaiser

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Deep learning has demonstrated great application value in several medical fields 9–11 . Recently, it has achieved comparable performance in pathological image analysis, including classification, 12 segmentation, 13 detection, 14 and assisted diagnosis 15–17 . Furthermore, deep learning is increasingly playing on pathological advanced tasks, including gene prediction, 18,19 survival analysis, 20,21 virtual staining, 22 etc.…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11] Recently, it has achieved comparable performance in pathological image analysis, including classification, 12 segmentation, 13 detection, 14 and assisted diagnosis. [15][16][17] Furthermore, deep learning is increasingly playing on pathological advanced tasks, including gene prediction, 18,19 survival analysis, 20,21 virtual staining, 22 etc. At present, most studies of deep learning in renal pathology focus on glomeruli, which are limited to isolated analysis of single level or staining, including segmentation and detection [23][24][25] and classification.…”

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

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The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.

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“…To solve the above problems, in a recent issue of eBioMedicine, the researchers proposed a deep learning-based OBMC prediction algorithm, which could extract key information of bone metastatic cancer from hematoxylin-eosin (HE) staining slides of biopsy tissues. 3 The input to their prediction model was the tumor region segmented by pathologists on HE slices. By modifying several state-of-the-art deep learning feature extractors, the researchers converted small patches in each region into structured feature vectors.…”

mentioning

confidence: 99%

Predicting origin for bone metastatic cancer using deep learning-based pathology

et al. 2023

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An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images

Cited by 7 publications

References 43 publications

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Predicting origin for bone metastatic cancer using deep learning-based pathology

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