A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

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Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.

Section: Introductionmentioning

confidence: 99%

Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Lang

Peeken

Combs

et al. 2021

Self Cite

“…The RSF was developed with 1000 trees. Hyperparameter optimization was conducted for node size (search space [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and the number of input variables randomly chosen at each node (mtry) (search space 2-10). For ENR, alpha (search space 0.05-1.0) and lambda were optimized.…”

Section: Modeling Strategymentioning

confidence: 99%

“…It is defined as an algorithm-based large-scale quantitative analysis of imaging features [ 12 , 13 , 14 , 15 ]. Such imaging biomarkers were shown to predict survival, tumor progression, spatial infiltration, and molecular aberrations in a multitude of cancer types [ 16 , 17 , 18 , 19 , 20 , 21 ]. In a recent publication, Spraker et al did demonstrate a prognostic potential of contrast-enhanced and fat-saturated T1-weighted (T1FSGd) sequence-based radiomics in STS patients [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Peeken

Neumann

Asadpour

et al. 2021

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Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

“…Delineation of ROI is conducted manually, semi-automatically or fully automatically. Manual segmentation requires experienced radiologists to contour the full tumor regions, which is still regarded as the gold standard in many applications [ 25 , 26 , 27 ]. Semi-automatic segmentation is usually realized by thresholding and region-growing techniques, which is more efficient as it requires less manual intervention [ 28 , 29 ].…”

Section: Machine Learning and Radiomics Workflow For Oncology Imagingmentioning

confidence: 99%

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Xie

Pang

Chan

et al. 2021

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.