Pathological prognosis classification of patients with neuroblastoma using computational pathology analysis

Liu, Yanfei; Jia, Yuxia; Hou, Chongzhi; Li, Nan; Zhang, Na; Yan, Xia; Li, Yang; Guo, Youguang; Chen, Huangtao; Li, Jun; Hao, Yingjie; Liu, Jixin

doi:10.1016/j.compbiomed.2022.105980

Cited by 6 publications

(9 citation statements)

References 33 publications

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“…Histological data that may be utilised by ML for patient prognosis predictions was also discussed in this review [ 17 , 39 , 41 ]. Similarly, DL (DNN) approaches were proposed by Gheisari et al to classify digital images of NB into five groups [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

“…The next step was the ML method for prognosis prediction, which included feature reduction, feature selection, and optimal model construction. For example, patient-level features were reduced to 25, after which the datasets were split by training and testing and underwent bootstrap resampling (1000×) [ 41 ]. A logistic regression was also used to incorporate features into a multivariate model, and then various feature combinations were tested and AUCs were generated.…”

Section: The Use Of Patient Data For Predicting Patient Outcomesmentioning

confidence: 99%

“…Optimal model construction also entailed boot-strapping resampling (1000×) with adjustments. For example, in both the training and testing datasets, parameters such as age and nucleus morphology intensity of features could accurately classify the patients (AUC of 0.946), while the validation dataset achieved an AUC of 0.938 ( Figure 4 C) [ 41 ].…”

Section: The Use Of Patient Data For Predicting Patient Outcomesmentioning

confidence: 99%

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Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Jahangiri

2024

Medical Sciences

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Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.

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Section: Discussionmentioning

confidence: 99%

Section: The Use Of Patient Data For Predicting Patient Outcomesmentioning

confidence: 99%

Section: The Use Of Patient Data For Predicting Patient Outcomesmentioning

confidence: 99%

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Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Jahangiri

2024

Medical Sciences

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“…Liu et al [ 174 ] advise a novel approach for neuroblastoma categorization using histopathological whole-slide images. The most frequent extra-cranial malignant tumor in young children is neuroblastoma.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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“…However, the assessment of INPC is complex and subjective, and the analysis of the same patient by multiple pathologists may lead to inconsistent results. Moreover, the pathological heterogeneity of neuroblastoma can cause different differentiation grades at different parts of the same tumor, thus reducing the accurate evaluation of INPC [ 8 , 9 ]. Besides, the evaluation of MKI, an indicator of INPC, involves manual counting of 5000 cells under a microscope to determine the total number of cells undergoing karyorrhexis or in mitosis, which is a lengthy and laborious process [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma

et al. 2023

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Purpose To predict the International Neuroblastoma Pathology Classification (INPC) in neuroblastoma using a computed tomography (CT)-based radiomics approach. Methods We enrolled 297 patients with neuroblastoma retrospectively and divided them into a training group (n = 208) and a testing group (n = 89). To balance the classes in the training group, a Synthetic Minority Over-sampling Technique was applied. A logistic regression radiomics model based on the radiomics features after dimensionality reduction was then constructed and validated in both the training and testing groups. To evaluate the diagnostic performance of the radiomics model, the receiver operating characteristic curve and calibration curve were utilized. Moreover, the decision curve analysis to assess the net benefits of the radiomics model at different high-risk thresholds was employed. Results Seventeen radiomics features were used to construct radiomics model. In the training group, radiomics model achieved an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.851 (95% confidence interval (CI) 0.805–0.897), 0.770, 0.694, and 0.847, respectively. In the testing group, radiomics model achieved an AUC, accuracy, sensitivity, and specificity of 0.816 (95% CI 0.725–0.906), 0.787, 0.793, and 0.778, respectively. The calibration curve indicated that the radiomics model was well fitted in both the training and testing groups (p > 0.05). Decision curve analysis further confirmed that the radiomics model performed well at different high-risk thresholds. Conclusion Radiomics analysis of contrast-enhanced CT demonstrates favorable diagnostic capabilities in distinguishing the INPC subgroups of neuroblastoma. Graphical Abstract Critical relevance statement Radiomics features of contrast-enhanced CT images correlate with the International Neuroblastoma Pathology Classification (INPC) of neuroblastoma.

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Pathological prognosis classification of patients with neuroblastoma using computational pathology analysis

Cited by 6 publications

References 33 publications

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma

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