1
|
Zlatanova T, Arabadjiev J, Kirova-Nedyalkova G, Nikova D. Successful treatment with docetaxel plus nintedanib in a patient with lung adenocarcinoma and pulmonary fibrosis: A case report and literature review. Front Oncol 2022; 12:907321. [PMID: 36016602 PMCID: PMC9396293 DOI: 10.3389/fonc.2022.907321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/14/2022] [Indexed: 11/30/2022] Open
Abstract
Despite the rare incidence of idiopathic pulmonary fibrosis (IPF), coexisting IPF and lung cancer is common. Both diseases have unfavorable outcomes and are often associated with impaired quality of life. In this study, we present a clinical case of a patient with coexisting IPF and lung adenocarcinoma who was successfully treated with nintedanib plus docetaxel as a second-line treatment, and achieved a substantial improvement in the quality of life. To our knowledge, very few cases in the literature address the concurrent treatment of both diseases, which makes this case a valuable illustration of a successful treatment strategy and a basis for future investigations.
Collapse
Affiliation(s)
- Tanya Zlatanova
- Department of Medical Oncology, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria
- *Correspondence: Tanya Zlatanova,
| | - Jeliazko Arabadjiev
- Department of Medical Oncology, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria
| | | | - Diana Nikova
- Clinic of Pneumology, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria
| |
Collapse
|
2
|
Sun D, Hadjiiski L, Alva A, Zakharia Y, Joshi M, Chan HP, Garje R, Pomerantz L, Elhag D, Cohan RH, Caoili EM, Kerr WT, Cha KH, Kirova-Nedyalkova G, Davenport MS, Shankar PR, Francis IR, Shampain K, Meyer N, Barkmeier D, Woolen S, Palmbos PL, Weizer AZ, Samala RK, Zhou C, Matuszak M. Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study. Tomography 2022; 8:644-656. [PMID: 35314631 PMCID: PMC8938803 DOI: 10.3390/tomography8020054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 11/22/2022] Open
Abstract
This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians’ diagnostic accuracy in assessing bladder cancer treatment response. The performance of 17 observers was evaluated when assessing bladder cancer treatment response without and with CDSS-T using pre- and post-chemotherapy CTU scans in 123 patients having 157 pre- and post-treatment cancer pairs. The impact of cancer case difficulty, observers’ clinical experience, institution affiliation, specialty, and the assessment times on the observers’ diagnostic performance with and without using CDSS-T were analyzed. It was found that the average performance of the 17 observers was significantly improved (p = 0.002) when aided by the CDSS-T. The cancer case difficulty, institution affiliation, specialty, and the assessment times influenced the observers’ performance without CDSS-T. The AI-based decision support system has the potential to improve the diagnostic accuracy in assessing bladder cancer treatment response and result in more consistent performance among all physicians.
Collapse
Affiliation(s)
- Di Sun
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
- Correspondence:
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Ajjai Alva
- Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA; (A.A.); (P.L.P.)
| | - Yousef Zakharia
- Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA; (Y.Z.); (R.G.); (D.E.)
| | - Monika Joshi
- Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA; (M.J.); (L.P.)
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Rohan Garje
- Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA; (Y.Z.); (R.G.); (D.E.)
| | - Lauren Pomerantz
- Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA; (M.J.); (L.P.)
| | - Dean Elhag
- Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA; (Y.Z.); (R.G.); (D.E.)
| | - Richard H. Cohan
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Elaine M. Caoili
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Wesley T. Kerr
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Kenny H. Cha
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD 20993, USA;
| | | | - Matthew S. Davenport
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Prasad R. Shankar
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Isaac R. Francis
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Kimberly Shampain
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Nathaniel Meyer
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Daniel Barkmeier
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Sean Woolen
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Phillip L. Palmbos
- Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA; (A.A.); (P.L.P.)
| | - Alon Z. Weizer
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Ravi K. Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA;
| |
Collapse
|
3
|
Hadjiiski LM, Cha KH, Cohan RH, Chan HP, Caoili EM, Davenport MS, Samala RK, Weizer AZ, Alva A, Kirova-Nedyalkova G, Shampain K, Meyer N, Barkmeier D, Woolen SA, Shankar PR, Francis IR, Palmbos PL. Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support. ACTA ACUST UNITED AC 2021; 6:194-202. [PMID: 32548296 PMCID: PMC7289252 DOI: 10.18383/j.tom.2020.00013] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We evaluated the intraobserver variability of physicians aided by a computerized decision-support system for treatment response assessment (CDSS-T) to identify patients who show complete response to neoadjuvant chemotherapy for bladder cancer, and the effects of the intraobserver variability on physicians' assessment accuracy. A CDSS-T tool was developed that uses a combination of deep learning neural network and radiomic features from computed tomography (CT) scans to detect bladder cancers that have fully responded to neoadjuvant treatment. Pre- and postchemotherapy CT scans of 157 bladder cancers from 123 patients were collected. In a multireader, multicase observer study, physician-observers estimated the likelihood of pathologic T0 disease by viewing paired pre/posttreatment CT scans placed side by side on an in-house-developed graphical user interface. Five abdominal radiologists, 4 diagnostic radiology residents, 2 oncologists, and 1 urologist participated as observers. They first provided an estimate without CDSS-T and then with CDSS-T. A subset of cases was evaluated twice to study the intraobserver variability and its effects on observer consistency. The mean areas under the curves for assessment of pathologic T0 disease were 0.85 for CDSS-T alone, 0.76 for physicians without CDSS-T and improved to 0.80 for physicians with CDSS-T (P = .001) in the original evaluation, and 0.78 for physicians without CDSS-T and improved to 0.81 for physicians with CDSS-T (P = .010) in the repeated evaluation. The intraobserver variability was significantly reduced with CDSS-T (P < .0001). The CDSS-T can significantly reduce physicians' variability and improve their accuracy for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Ajjai Alva
- Internal Medicine, Division of Hematology-Oncology, University of Michigan, Ann Arbor, MI
| | | | | | | | | | - Sean A Woolen
- Department of Radiology, University of California, San Francisco, Medical Center, San Francisco, CA
| | | | | | - Phillip L Palmbos
- Internal Medicine, Division of Hematology-Oncology, University of Michigan, Ann Arbor, MI
| |
Collapse
|
4
|
Georgiev E, Radeva R, Naseva E, Kirova-Nedyalkova G. COMPARISON OF RADIATION DOSE AND IMAGE QUALITY IN CTA OF THE PERIPHERAL ARTERIES. Radiat Prot Dosimetry 2019; 186:437-442. [PMID: 31034552 DOI: 10.1093/rpd/ncz045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/25/2019] [Accepted: 03/12/2019] [Indexed: 06/09/2023]
Abstract
The aim of this study is to investigate the possibility of replacing the standard CTA protocol for peripheral arteries with a low dose CTA protocol without affecting the diagnostic image quality. Therefore a single centre retrospective study was conducted involving 200 exams of patients undergoing lower limb angiography. All exams were performed on a 64-row detector CT and the vascular density, muscle density, noise and radiation dose of each image were assessed. The subjective image quality was evaluated additionally by an experienced radiologist. Significant differences were observed in radiation dose and image quality between the standard CTA protocol and the lower dose CTA protocol. No differences were found between objective and subjective image quality. Using 80kVp instead of 120kVp as the tube voltage for lower limb CTA reduces the radiation dose without affecting the diagnostic image quality.
Collapse
Affiliation(s)
- Emil Georgiev
- Radiology Department, Acibadem City Clinic Tokuda Hospital, 51B 'Nikola I. Vaptsarov' Blvd., Sofia, Bulgaria
| | - Radina Radeva
- Radiology Department, Acibadem City Clinic Tokuda Hospital, 51B 'Nikola I. Vaptsarov' Blvd., Sofia, Bulgaria
| | - Emilia Naseva
- Radiology Department, Acibadem City Clinic Tokuda Hospital, 51B 'Nikola I. Vaptsarov' Blvd., Sofia, Bulgaria
| | - Galina Kirova-Nedyalkova
- Radiology Department, Acibadem City Clinic Tokuda Hospital, 51B 'Nikola I. Vaptsarov' Blvd., Sofia, Bulgaria
| |
Collapse
|
5
|
Cha KH, Hadjiiski LM, Cohan RH, Chan HP, Caoili EM, Davenport MS, Samala RK, Weizer AZ, Alva A, Kirova-Nedyalkova G, Shampain K, Meyer N, Barkmeier D, Woolen S, Shankar PR, Francis IR, Palmbos P. Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support. Acad Radiol 2019; 26:1137-1145. [PMID: 30424999 DOI: 10.1016/j.acra.2018.10.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 09/23/2018] [Accepted: 10/09/2018] [Indexed: 10/27/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate whether a computed tomography (CT)-based computerized decision-support system for muscle-invasive bladder cancer treatment response assessment (CDSS-T) can improve identification of patients who have responded completely to neoadjuvant chemotherapy. MATERIALS AND METHODS Following Institutional Review Board approval, pre-chemotherapy and post-chemotherapy CT scans of 123 subjects with 157 muscle-invasive bladder cancer foci were collected retrospectively. CT data were analyzed with a CDSS-T that uses a combination of deep-learning convolutional neural network and radiomic features to distinguish muscle-invasive bladder cancers that have fully responded to neoadjuvant treatment from those that have not. Leave-one-case-out cross-validation was used to minimize overfitting. Five attending abdominal radiologists, four diagnostic radiology residents, two attending oncologists, and one attending urologist estimated the likelihood of pathologic T0 disease (complete response) by viewing paired pre/post-treatment CT scans placed side-by-side on an internally-developed graphical user interface. The observers provided an estimate without use of CDSS-T and then were permitted to revise their estimate after a CDSS-T-derived likelihood score was displayed. Observer estimates were analyzed with multi-reader, multi-case receiver operating characteristic methodology. The area under the curve (AUC) and the statistical significance of the difference were estimated. RESULTS The mean AUCs for assessment of pathologic T0 disease were 0.80 for CDSS-T alone, 0.74 for physicians not using CDSS-T, and 0.77 for physicians using CDSS-T. The increase in the physicians' performance was statistically significant (P < .05). CONCLUSION CDSS-T improves physician performance for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.
Collapse
|