Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

Fujima, Noriyuki; Shimizu, Yukie; Yoshida, Daisuke; Kano, Satoshi; Mizumachi, Takatsugu; Homma, Akihiro; Yasuda, Kôichi; Onimaru, Rikiya; Sakai, Osamu; Kudo, Kohsuke; Shirato, Hiroki

doi:10.3390/cancers11060800

Cited by 40 publications

(38 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, a comprehensive set of texture features, including gray-level run-length matrix (GLRLM) [25] and gray-level co-occurrence matrix (GLCM) [26], was extracted to summarize the content and distribution of chromatin within nuclei, thereby leading to 18 features per image. Texture features have been extensively used in the past for developing diagnostic and prognostic indices [27,28]. To obtain these texture features in 2 dimensions, all images were first quantized to 16 gray levels.…”

Section: Image Processing and Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

Rathore

Niazi

Iftikhar

et al. 2020

Cancers

View full text Add to dashboard Cite

Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.65 years, male: 427, female: 308, median survival: 761.26 days) were obtained from TCGA. Sub-images that contained a viable tumor area, showing sufficient histologic characteristics, and that did not have any staining artifact were extracted. Several clinical measures and imaging features, including conventional (intensity, morphology) and advanced textures features (gray-level co-occurrence matrix and gray-level run-length matrix), extracted from the sub-images were further used for training the support vector machine model with linear configuration. We sought to evaluate the combined effect of conventional imaging, clinical, and texture features by assessing the predictive value of each feature type and their combinations through a predictive classifier. The texture features were successfully validated on the glioma patients in 10-fold cross-validation (accuracy = 75.12%, AUC = 0.652). The addition of texture features to clinical and conventional imaging features improved grade prediction compared to the models trained on clinical and conventional imaging features alone (p = 0.045 and p = 0.032 for conventional imaging features and texture features, respectively). The integration of imaging, texture, and clinical features yielded a significant improvement in accuracy, supporting the synergistic value of these features in the predictive model. The findings suggest that the texture features, when combined with conventional imaging and clinical markers, may provide an objective, accurate, and integrated prediction of glioma grades. The proposed digital pathology imaging-based marker may help to (i) stratify patients into clinical trials, (ii) select patients for targeted therapies, and (iii) personalize treatment planning on an individual person basis.

show abstract

Section: Image Processing and Feature Extractionmentioning

confidence: 99%

“…Texture features have been extensively used in the past for developing diagnostic and prognostic indices [27,28]. To obtain these texture features in 2 dimensions, all images were first quantized to 16 gray levels.…”

Section: Image Processing and Feature Extractionmentioning

confidence: 99%

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

Rathore

Niazi

Iftikhar

et al. 2020

Cancers

View full text Add to dashboard Cite

show abstract

“…The prognosis for patients with brain metastases (BM) is known to be poor, as BM is one of the most deadly among various types of cancers [1,2,3,4]. Ranging from early detection to intervention therapy, many innovative management models have been formulated with the goal of lowering the fatality rate of BM.…”

Section: Introductionmentioning

confidence: 99%

Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

Huang¹,

Yang

Fong

et al. 2019

Cancers

View full text Add to dashboard Cite

This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods were selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set with particle swarm optimization (MIRSPSO) methods to predict patient’s prognosis with the highest accuracy at area under the curve (AUC) = 0.978 ± 0.06. The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29%, and 1.83% higher than that of the traditional statistical method, sequential feature selection (SFS), mutual information with particle swarm optimization(MIPSO), and mutual information with sequential feature selection (MISFS), respectively. Furthermore, the clinical performance of the best prognosis was superior to conventional statistic method in accuracy, sensitivity, and specificity. In conclusion, identifying optimal machine-learning methods for the prediction of overall survival in brain metastases is essential for clinical applications. The accuracy rate by machine-learning is far higher than that of conventional statistic methods.

show abstract

“…Table 1 shows a summary of the topics and the papers included for each topic [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The majority of papers are focused on prostate cancer (PCa) and radiomics.…”

mentioning

confidence: 99%

“…The second group by Schiano et al [ 32 ] showed that the combination of radiomic features by FDG PET/MRI and molecular data are able to predict the synchronous metastatic disease more accurately than a single information. Fujima et al [ 33 ] found a correlation between the clinical outcome and machine-learning algorithm using various MRI-derived data in patients with sinonasal squamous cell carcinomas.…”

mentioning

confidence: 99%