Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support

Kenny, H.; Hadjiiski, Lubomir M.; Cohan, Richard H.; Chan, Heang Ping; Caoili, Elaine M.; Davenport, Matthew S.; Samala, Ravi K.; Weizer, Alon Z.; Alva, Ajjai; Kirova-Nedyalkova, Galina; Shampain, Kimberly L.; Meyer, Nathaniel; Barkmeier, Daniel; Woolen, Sean; Shankar, Prasad R.; Francis, Isaac R.; Palmbos, Phillip L.

doi:10.1016/j.acra.2018.10.010

Cited by 56 publications

(40 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, radiomics is mainly used in oncology to facilitate improved clinical decision making . Indeed, radiomics can also be applied in patients with BCa, such as predicting lymph node metastasis and treatment response . In addition, 2 MRI‐based radiomics signatures to differentiate MIBC from NMIBC have been developed .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Zheng

Kong

et al. 2019

Cancer

View full text Add to dashboard Cite

Background Bladder cancer (BCa) can be divided into muscle‐invasive BCa (MIBC) and non−muscle‐invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic‐clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC. Methods In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2‐weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic‐clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69). Results The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging‐determined tumor size, the radiomic‐clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram. Conclusions The proposed noninvasive radiomic‐clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.

show abstract

Section: Introductionmentioning

confidence: 99%

“…[18][19][20] Indeed, radiomics can also be applied in patients with BCa, such as predicting lymph node metastasis and treatment response. [21][22][23][24] In addition, 2 MRI-based radiomics signatures to differentiate MIBC from NMIBC have been developed. 25,26 However, further studies are warranted because of limitations.…”

Section: Introductionmentioning

confidence: 99%

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Zheng

Kong

et al. 2019

Cancer

View full text Add to dashboard Cite

show abstract

“…Subsequent radiomics was used to classify the images. The validation paper in this issue (1) shows that this approach stands up in prospective clinical validation.…”

Section: Henkjan Huisman Phdmentioning

confidence: 95%

“…This issue of Academic Radiology contains a commendable study titled "Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support" (1). It describes an observer study of 10 clinicians using a computerized decision support system (CDSS) developed by the group (2) to discriminate responders from nonresponders in a set of 123 subjects undergoing neoadjuvant chemotherapy.…”

Section: Henkjan Huisman Phdmentioning

confidence: 99%

Solid Science of AI Supporting Bladder Cancer CT Reading

Huisman

2019

Academic Radiology

View full text Add to dashboard Cite

“…These rapid advances in artificial intelligence have led to some predictions of the doom of radiology as a medical specialty driven by human intelligence [12]. These claims are supported by results from recent studies like that of Cha et al in 2017, in which a deep learning algorithm was less accurate in detecting recurrent bladder cancer when humans participated in the interpretation [13]. More temperate prophets envision a future in which humans and machines can coexist, interpreting imaging studies in peaceful harmony [14].…”

mentioning

confidence: 93%

On High Grade Kidney Cancer and Machine Learning

Curci

2020

Academic Radiology

View full text Add to dashboard Cite

Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support

Cited by 56 publications

References 12 publications

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

Solid Science of AI Supporting Bladder Cancer CT Reading

On High Grade Kidney Cancer and Machine Learning

Contact Info

Product

Resources

About