Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers

Lafata, Kyle; Chang, Yushi; Wang, Chunhao; Mowery, Yvonne M.; Vergalasova, Irina; Niedzwiecki, Donna; Yoo, David S.; Liu, Jianguo; Brizel, D.M.; Yin, Fang‐Fang

doi:10.1002/mp.14926

Cited by 20 publications

(19 citation statements)

References 70 publications

(164 reference statements)

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“…A total of 48 articles met the inclusion criteria in the group of head and neck malignancies [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ], 46/48 (95.8%) using 18F-FDG. Only 1 study out of 48 (2.1%) used 18F-FMISO [ 36 ] and 1/48 (2.1%) [ 42 ] used 18F-FLT.…”

Section: Resultsmentioning

confidence: 99%

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

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Section: Resultsmentioning

confidence: 99%

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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“…Radiomics has been widely studied as computational imaging biomarkers for disease detection and outcome monitoring, 37 and it has been demonstrated that radiomic feature spaces can be mathematically decomposed to provide interpration. 38 Following the saliency map analysis approach in this work, additional parameters can be used to enhance deep learning interpretability, such as histology sample images from biopsy and anatomy contours from radiation therapy. These directions will be studied in future works when appropriate datasets become available.…”

Section: Discussionmentioning

confidence: 99%

“…As a step towards deep learning interpretability, we investigated neural network attention information using a radiomics‐based analysis. Radiomics has been widely studied as computational imaging biomarkers for disease detection and outcome monitoring, 37 and it has been demonstrated that radiomic feature spaces can be mathematically decomposed to provide interpration 38 . Following the saliency map analysis approach in this work, additional parameters can be used to enhance deep learning interpretability, such as histology sample images from biopsy and anatomy contours from radiation therapy.…”

Section: Discussionmentioning

confidence: 99%

A radiomics‐boosted deep‐learning model for COVID‐19 and non‐COVID‐19 pneumonia classification using chest x‐ray images

Yang

Lafata

et al. 2022

Medical Physics

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Purpose To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID‐19 and non‐COVID‐19 pneumonia detection using chest x‐ray images. Methods As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x‐ray image; thus, each feature is rendered as a 2D map in the same dimension as the x‐ray image. Based on each of the three investigated deep neural network architectures, including VGG‐16, VGG‐19, and DenseNet‐121, a pilot model was trained using x‐ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross‐correlation analysis in reference to the pilot model saliency map results. The radiomics‐boosted model was then trained based on the same deep neural network architecture using x‐ray images plus the selected RFMs as input. The proposed radiomics‐boosted design was developed using 812 chest x‐ray images with 262/288/262 COVID‐19/non‐COVID‐19 pneumonia/healthy cases, and 649/163 cases were assigned as training‐validation/independent test sets. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training‐validation set. Sensitivity, specificity, accuracy, and ROC curves together with area‐under‐the‐curve (AUC) from all three deep neural network architectures were evaluated. Results After radiomics‐boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID‐19 and healthy individual classifications. VGG‐16 showed the largest improvement in COVID‐19 classification ROC (AUC from 0.963 to 0.993), and DenseNet‐121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging non‐COVID‐19 pneumonia classification task, radiomics‐boosted implementation of VGG‐16 (AUC from 0.918 to 0.969) and VGG‐19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet‐121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID‐19/non‐COVID‐19 pneumonia/healthy individual classifications were 0.973 (VGG‐19)/0.936 (VGG‐19)/ 0.933 (VGG‐16), respectively. Conclusions The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID‐19/non‐COVID‐19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID‐19 pandemic.

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“…As a feasibility study, the current results showed that the achieved post-20-Gy 18 FDG-PET image outcome prediction had good agreement with ground-truth results. Post-20-Gy 18 FDG-PET has been demonstrated as informing surrogates of recurrence-free survival and overall survival of human papillomavirus (HPV)-related oropharyngeal cancer (38). In a potential clinical application scenario, the current framework would allow a physician to determine if an 18 FDG-PET scan after 20-Gy radiation would facilitate improved adaptive radiotherapy clinical decision making.…”

Section: Discussionmentioning

confidence: 99%

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application

Lafata

Mowery

et al. 2022

Front. Oncol.

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PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted.ConclusionThe developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

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Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers

Cited by 20 publications

References 70 publications

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

A radiomics‐boosted deep‐learning model for COVID‐19 and non‐COVID‐19 pneumonia classification using chest x‐ray images

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application

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