Deep Learning–based Detection of Intravenous Contrast Enhancement on CT Scans

Ye, Zezhong; Qian, Jack M.; Hosny, Ahmed; Zeleznik, Roman; Plana, Deborah; Likitlersuang, Jirapat; Zhang, Zhongyi; Mak, Raymond H.; Aerts, Hugo J.W.L.; Kann, Benjamin H.

doi:10.1148/ryai.210285

Cited by 16 publications

(17 citation statements)

References 13 publications

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“…This performance is consistent with previous studies exploring 2D architectures of four phase classification. 5,7,12 Similar to prior studies, 4,7 we found that only 15% of our dataset had informative phase descriptions in the DICOM header. This highlights the need for automated methods to annotate the phase of CT scans to aid in high quality dataset curation which is essential for deep learning algorithmic development.…”

Section: Discussionsupporting

confidence: 86%

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Automated classification of intravenous contrast enhancement phase of CT scans using residual networks

Anand¹,

Liu²,

Shen³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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Intravenous contrast enhancement phase information is important for computer-aided diagnosis of CT scans because the visual appearance of the scans varies substantially among the different phases. Although phase information could help to refine training data curation for downstream tasks, it is seldom included in the process of data augmentation for training a deep learning model. Unfortunately, in the current clinical settings, phase information is either unavailable or unreliable in most PACS systems. This motivates us to develop a method to automatically classify multiphase CT scans. In this study, a residual network (ResNet34) was utilized to classify five CT phases commonly used in the clinical environment: non-contrast, arterial, portal venous, nephrographic, and delayed contrast phases. A dataset of 395 multiphase CT scans was weakly labeled using keywords. The weakly-labeled dataset was split into 316 training, and 79 test CT scans. We compared the ResNet34 with two other popular classification models, VGG19 and DenseNet121. ResNet34 achieved the highest accuracy of 99%, while the accuracy of VGG19 and DenseNet121 were 97% and 95%, respectively. In addition, ResNet34 had fewer parameters to train in comparison with two other models, which could reduce the inference time to 35 seconds per scan and enhance generalizability of the model. High accuracy of multiphase classification suggests a potential way to improve data curation based on CT contrast enhancement phase. This would be useful to improve deep learning models by enhancing dataset curation and providing more realistic augmented data.

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Section: Discussionsupporting

confidence: 86%

“…A 2D convolution neural network was used to automatically classify contrast and non-contrast CT scans. 3,4 It was expanded to predict four contrast phases including non-contrast, arterial, portal venous, and delayed later on. [5][6][7][8] This work extends the task to five phase classification through the addition of nephrographic phase.…”

Section: Introductionmentioning

confidence: 99%

Automated classification of intravenous contrast enhancement phase of CT scans using residual networks

Anand¹,

Liu²,

Shen³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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show abstract

“…Dose coverage was compared 25 Lesion volume as calculated from our AI-generated segmen tations was compared with that of three previously published segmentation models. 26 Model performance based on the use of intravenous contrast in images (detected using a published algorithm 27 ) was assessed through subgroup analysis. DSB conducted the model failure mode analysis by qualitatively assessing model results on the RTOG-0617 dataset and identifying cases of under-segmentation or over-segmentation.…”

Section: Functional Validationmentioning

confidence: 99%

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study

Hosny¹,

Bitterman²,

Guthier³

et al. 2022

The Lancet Digital Health

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“…A fully automated, accurate SMI assessment pipeline is thus necessary for clinical integration and utility in the management and monitoring of HNSCC. In past years, multiple deep learning models have been created and extensively used for medical imaging . Although recent studies have applied deep learning techniques to determine skeletal muscle through abdominal CT scans on the L3 vertebral level, few have been performed in head and neck cancer, a disease that has been increasing in prevalence and is known for its challenges in terms of patient vulnerability, treatment decisions, and long-term adverse effects.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer

Saraf

Ravipati

et al. 2023

JAMA Netw Open

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ImportanceSarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use.ObjectiveTo develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes.Design, Setting, and ParticipantsFor this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women’s Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023.ExposureC3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC.Main Outcomes and MeasuresOverall survival and treatment toxicity outcomes of HNSCC.ResultsThe total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P &lt; .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia.Conclusions and RelevanceThis prognostic study’s findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

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Deep Learning–based Detection of Intravenous Contrast Enhancement on CT Scans

Cited by 16 publications

References 13 publications

Automated classification of intravenous contrast enhancement phase of CT scans using residual networks

Automated classification of intravenous contrast enhancement phase of CT scans using residual networks

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer

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