Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study

Sun, Di; Hadjiiski, Lubomir M.; Alva, Ajjai; Zakharia, Yousef; Joshi, Monika; Chan, Heang-Ping; Garje, Rohan; Pomerantz, Lauren; Elhag, Dean; Cohan, Richard H.; Caoili, Elaine M.; Kerr, Wesley T.; Kenny, H.; Kirova-Nedyalkova, Galina; Davenport, Matthew S.; Shankar, Prasad R.; Francis, Isaac R.; Shampain, Kimberly L.; Meyer, Nathaniel; Barkmeier, Daniel; Woolen, Sean; Palmbos, Phillip L.; Weizer, Alon Z.; Samala, Ravi K.; Zhou, Chuan; Matuszak, Martha M.

doi:10.3390/tomography8020054

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…This has been shown to be the case for ML tools developed to detect prostate cancer from MRI scans, which did not persuade physicians to change their diagnostic decision in the rare instance that an incorrect probability was assigned by the algorithm. But, overall the AI tool improved physicians' diagnostic accuracy and reduced physicians' variability (Sun et al, 2022).…”

Section: Artificial Intelligencementioning

confidence: 99%

RNA biomarkers for alcohol use disorder

Ferguson

Mayfield

Messing

2022

Front. Mol. Neurosci.

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Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

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Section: Artificial Intelligencementioning

confidence: 99%

RNA biomarkers for alcohol use disorder

Ferguson

Mayfield

Messing

2022

Front. Mol. Neurosci.

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“…However, neoadjuvant chemotherapy has substantial side effects including neutropenia, granulocytopenia, sepsis, mucositis, nausea, and vomiting [ 125 ]. It is therefore of pivotal importance to select patients who will respond to these treatments to avoid the toxicity in potentially unresponsive patients and to provide alternative therapies to unresponsive patients [ 126 ]; moreover, if a patient can be reliably identified as having a complete response to treatment, the option of organ preservation therapy instead of cystectomy may be considered [ 127 ].…”

Section: Artificial Intelligencementioning

confidence: 99%

Computed Tomography Urography: State of the Art and Beyond

et al. 2023

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Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients’ outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

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“…We implemented and validated a DL-CNN structure in TensorFlow 7 to assess the cancer treatment response (T0 versus >T0) 8 . The DL-CNN used in this study consisted of two convolutional layers (C1 and C2), two locally connected layers (L3 and L4), and a fully connected layer (FC10).…”

Section: Dl-cnn Modelmentioning

confidence: 99%

“…The classification process is shown in Figure 4. Radiomics model: For the radiomics model, we extracted 91 features from the segmented pre-and post-treatment lesions, including shape, size, and texture features 8 . The random forest classifier was employed to classify CRs and NCRs, guided by the validation set to select the parameter settings.…”

Section: Dl-cnn Modelmentioning

confidence: 99%

Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics

Sun¹,

Hadjiiski²,

Chan³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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We are developing deep-learning convolutional neural network (DL-CNN) and radiomics models to assist physicians in treatment response assessment in CT urography of bladder cancer to neoadjuvant chemotherapy (NAC). We collected a total of 264 pre-and post-treatment lesion pairs of 227 patients from University of Michigan hospital with IRB approval. The data were split into 3 sets by case: a training set including 35 complete responders (CRs) (T0 stage after treatment) and 113 non-complete responders (NCRs) (> T0 stage after treatment), a validation set including 5 CRs and 5 NCRs, and an independent test set including 19 CRs and 87 NCRs. The training set was used to train the models to classify CRs and NCRs and the selection of optimal models was guided by the validation set. The selected models were deployed on the test set to generate the likelihood score of CR of each pair. The classifying performance was evaluated by the area under ROC curve (AUC). Hybrid ROIs extracted from the lesions in the pre-and post-treatment scan pairs were used as input to the DL-CNN model. The optimal DL-CNN model achieved an AUC of 0.75 ± 0.06 on the test set. For the radiomics model, the random forest classifier was applied to the features extracted from the pre-and posttreatment lesions. The optimal radiomics model achieved an AUC of 0.76 ± 0.05. A combined DL-CNN and radiomics model increased the AUC to 0.77 ± 0.06. The results indicated the feasibility of using the DL-CNN model and radiomics model for assessing treatment response of bladder cancer.

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Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study

Cited by 10 publications

References 16 publications

RNA biomarkers for alcohol use disorder

RNA biomarkers for alcohol use disorder

Computed Tomography Urography: State of the Art and Beyond

Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics

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