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Causa Andrieu PI, Patel-Lippmann KK. Commentary on "CT angiography for characterization of advanced placenta accreta spectrum: indications, risk and benefits". Abdom Radiol (NY) 2024; 49:855-856. [PMID: 38195801 DOI: 10.1007/s00261-023-04169-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/16/2023] [Indexed: 01/11/2024]
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Charbel C, Causa Andrieu PI, Soliman M, Woo S, Zheng J, Capanu M, Nikolovski I, Vargas HA, Abusamra M, Carlo MI. The Prevalence and Radiologic Features of Renal Cancers Associated with FLCN, BAP1, SDH, and MET Germline Mutations. Radiol Imaging Cancer 2024; 6:e230063. [PMID: 38456787 PMCID: PMC10988346 DOI: 10.1148/rycan.230063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 12/15/2023] [Accepted: 01/30/2024] [Indexed: 03/09/2024]
Abstract
Purpose To investigate the prevalence of FLCN, BAP1, SDH, and MET mutations in an oncologic cohort and determine the prevalence, clinical features, and imaging features of renal cell carcinoma (RCC) associated with these mutations. Secondarily, to determine the prevalence of encountered benign renal lesions. Materials and Methods From 25 220 patients with cancer who prospectively underwent germline analysis with a panel of more than 70 cancer-predisposing genes from 2015 to 2021, patients with FLCN, BAP1, SDH, or MET mutations were retrospectively identified. Clinical records were reviewed for patient age, sex, race/ethnicity, and renal cancer diagnosis. If RCC was present, baseline CT and MRI examinations were independently assessed by two radiologists. Summary statistics were used to summarize continuous and categorical variables by mutation. Results A total of 79 of 25 220 (0.31%) patients had a germline mutation: FLCN, 17 of 25 220 (0.07%); BAP1, 22 of 25 220 (0.09%); SDH, 39 of 25 220 (0.15%); and MET, one of 25 220 (0.004%). Of these 79 patients, 18 (23%) were diagnosed with RCC (FLCN, four of 17 [24%]; BAP1, four of 22 [18%]; SDH, nine of 39 [23%]; MET, one of one [100%]). Most hereditary RCCs demonstrated ill-defined margins, central nonenhancing area (cystic or necrotic), heterogeneous enhancement, and various other CT and MR radiologic features, overlapping with the radiologic appearance of nonhereditary RCCs. The prevalence of other benign solid renal lesions (other than complex cysts) in patients was up to 11%. Conclusion FLCN, BAP1, SDH, and MET mutations were present in less than 1% of this oncologic cohort. Within the study sample size limits, imaging findings for hereditary RCC overlapped with those of nonhereditary RCC, and the prevalence of other associated benign solid renal lesions (other than complex cysts) was up to 11%. Keywords: Familial Renal Cell Carcinoma, Birt-Hogg-Dubé Syndrome, Carcinoma, Renal Cell, Paragangliomas, Urinary, Kidney © RSNA, 2024.
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Affiliation(s)
| | | | - Mohamed Soliman
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Sungmin Woo
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Junting Zheng
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Marinela Capanu
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Ines Nikolovski
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Hebert A. Vargas
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Murad Abusamra
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
| | - Maria I. Carlo
- From the Department of Radiology, Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (C.C.);
Department of Radiology, Mayo Clinic, Rochester, Minn (P.I.C.A.); Department of
Radiology (M.S.), Department of Epidemiology and Biostatistics (J.Z., M.C.), and
Genitourinary Oncology Service (M.I.C.), Memorial Sloan Kettering Cancer Center,
New York, NY; Department of Radiology, NYU Langone Health, New York, NY (S.W.,
H.A.V.); Department of Radiology, Royal North Shore Hospital, St Leonards, New
South Wales, Australia (I.N.); and Department of Radiology, Cleveland Clinic,
Cleveland, Ohio (M.A.)
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Wong BZY, Causa Andrieu PI, Sonoda Y, Chi DS, Aviki EM, Vargas HA, Woo S. Improving risk stratification of indeterminate adnexal masses on MRI: What imaging features help predict malignancy in O-RADS MRI 4 lesions? Eur J Radiol 2023; 168:111122. [PMID: 37806193 DOI: 10.1016/j.ejrad.2023.111122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/07/2023] [Accepted: 09/27/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE Ovarian-Adnexal Reporting and Data System (O-RADS) MRI uses a 5-point scale to establish malignancy risk in sonographically-indeterminate adnexal masses. The management of O-RADS MRI score 4 lesions is challenging, as the prevalence of malignancy is widely variable (5-90%). We assessed imaging features that may sub-stratify O-RADS MRI 4 lesions into malignant and benign subgroups. METHOD Retrospective single-institution study of women with O-RADS MRI score of 4 adnexal masses between April 2021-August 2022. Imaging findings were assessed independently by 2 radiologists according to the O-RADS lexicon white paper. MRI and clinical findingswere compared between malignant and benign adnexal masses, and inter-reader agreement was calculated. RESULTS Seventy-four women (median age 52 years, IQR 36-61) were included. On pathology, 41 (55.4%) adnexal masses were malignant. Patients with malignant masses were younger (p = 0.02) with higher CA-125 levels (p = 0.03). Size of solid tissue was greater in malignant masses (p = 0.01-0.04). Papillary projections and larger solid portion were more common in malignant lesions; irregular septations and predominantly solid composition were more frequent in benign lesions (p < 0.01). Solid tissue of malignant lesions was more often hyperintense on T2-weighted and diffusion-weighted imaging (p ≤ 0.03). Other imaging findings were not significantly different (p = 0.09-0.77). Inter-reader agreement was excellent-good for most features (ICC = 0. 662-0.950; k = 0. 650-0.860). CONCLUSION Various MRI and clinical features differed between malignant and benign O-RADS MRI score 4 adnexal masses. O-RADS MRI 4 lesions may be sub-stratified (high vs low risk) based on solid tissue characteristics and CA-125 levels.
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Affiliation(s)
- Bernadette Z Y Wong
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Pamela I Causa Andrieu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Yukio Sonoda
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Dennis S Chi
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Emeline M Aviki
- Department of Obstetrics and Gynecology, NYU Langone Health, Mineola, NY 11501, USA
| | - Hebert A Vargas
- Department of Radiology, NYU Langone Health, New York, NY 10016, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology, NYU Langone Health, New York, NY 10016, USA.
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Causa Andrieu PI, Wahab SA, Nougaret S, Petkovska I. Ovarian cancer during pregnancy. Abdom Radiol (NY) 2023; 48:1694-1708. [PMID: 36538079 PMCID: PMC10627077 DOI: 10.1007/s00261-022-03768-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 05/01/2023]
Abstract
Adnexal masses during pregnancy are a relatively uncommon entity. Their clinical management is challenging given the overlapping features of certain entities on imaging and histopathology, which can mimic malignancy, and the potential side effects to the mother and fetus, whether expectant management versus surgery is pursued. Ultrasonography with Doppler evaluation is the modality of choice for evaluating adnexal masses during pregnancy. Magnetic resonance imaging is the second-line modality useful when US findings are inconclusive/indeterminate. Most adnexal masses in pregnant patients are benign in origin (e.g., functional cysts, mature cystic teratoma, decidualization of endometrioma), but a few are malignant in origin (e.g., dysgerminoma, granulosa cell tumor). Most cases of adnexal masses are asymptomatic, but complications such as ovarian torsion can occur. This review aims to familiarize the radiologist with the imaging of adnexal lesions during pregnancy so that the radiologist can identify ovarian cancer. Specifically, the review will detail the most common benign and malignant adnexal masses in pregnancy, mimickers, and their corresponding imaging findings on US and MRI.
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Affiliation(s)
- Pamela I Causa Andrieu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
| | - Shaun A Wahab
- Department of Radiology, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Stephanie Nougaret
- Department of Radiology, Cancer Institute of Montpellier, Montpellier, France
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
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Cheng M, Duzgol C, Kim TH, Ghafoor S, Becker AS, Causa Andrieu PI, Gangai N, Jiang H, Hakimi AA, Vargas HA, Woo S. Sarcomatoid renal cell carcinoma: MRI features and their association with survival. Cancer Imaging 2023; 23:16. [PMID: 36793052 PMCID: PMC9930281 DOI: 10.1186/s40644-023-00535-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
OBJECTIVE To evaluate MRI features of sarcomatoid renal cell carcinoma (RCC) and their association with survival. METHODS This retrospective single-center study included 59 patients with sarcomatoid RCC who underwent MRI before nephrectomy during July 2003-December 2019. Three radiologists reviewed MRI findings of tumor size, non-enhancing areas, lymphadenopathy, and volume (and percentage) of T2 low signal intensity areas (T2LIA). Clinicopathological factors of age, gender, ethnicity, baseline metastatic status, pathological details (subtype and extent of sarcomatoid differentiation), treatment type, and follow-up were extracted. Survival was estimated using Kaplan-Meier method and Cox proportional-hazards regression model was used to identify factors associated with survival. RESULTS Forty-one males and eighteen females (median age 62 years; interquartile range 51-68) were included. T2LIAs were present in 43 (72.9%) patients. At univariate analysis, clinicopathological factors associated with shorter survival were: greater tumor size (> 10 cm; HR [hazard ratio] = 2.44, 95% CI 1.15-5.21; p = 0.02), metastatic lymph nodes (present; HR = 2.10, 95% CI 1.01-4.37; p = 0.04), extent of sarcomatoid differentiation (non-focal; HR = 3.30, 95% CI 1.55-7.01; p < 0.01), subtypes other than clear cell, papillary, or chromophobe (HR = 3.25, 95% CI 1.28-8.20; p = 0.01), and metastasis at baseline (HR = 5.04, 95% CI 2.40-10.59; p < 0.01). MRI features associated with shorter survival were: lymphadenopathy (HR = 2.24, 95% CI 1.16-4.71; p = 0.01) and volume of T2LIA (> 3.2 mL, HR = 4.22, 95% CI 1.92-9.29); p < 0.01). At multivariate analysis, metastatic disease (HR = 6.89, 95% CI 2.79-16.97; p < 0.01), other subtypes (HR = 9.50, 95% CI 2.81-32.13; p < 0.01), and greater volume of T2LIA (HR = 2.51, 95% CI 1.04-6.05; p = 0.04) remained independently associated with worse survival. CONCLUSION T2LIAs were present in approximately two thirds of sarcomatoid RCCs. Volume of T2LIA along with clinicopathological factors were associated with survival.
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Affiliation(s)
- Monica Cheng
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA ,grid.38142.3c000000041936754XDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Cihan Duzgol
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA ,grid.461527.30000 0004 0383 4123Department of Radiology, Lowell General Hospital, 295 Varnum Avenue, Lowell, MA 01854, USA
| | - Tae-Hyung Kim
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA
| | - Soleen Ghafoor
- grid.412004.30000 0004 0478 9977Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Anton S. Becker
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA
| | - Pamela I. Causa Andrieu
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA
| | - Natalie Gangai
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA
| | - Hui Jiang
- grid.51462.340000 0001 2171 9952Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Abraham A. Hakimi
- grid.51462.340000 0001 2171 9952Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Hebert A. Vargas
- grid.51462.340000 0001 2171 9952Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
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Boehm KM, Aherne EA, Ellenson L, Nikolovski I, Alghamdi M, Vázquez-García I, Zamarin D, Long Roche K, Liu Y, Patel D, Aukerman A, Pasha A, Rose D, Selenica P, Causa Andrieu PI, Fong C, Capanu M, Reis-Filho JS, Vanguri R, Veeraraghavan H, Gangai N, Sosa R, Leung S, McPherson A, Gao J, Lakhman Y, Shah SP. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer 2022; 3:723-733. [PMID: 35764743 PMCID: PMC9239907 DOI: 10.1038/s43018-022-00388-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 04/27/2022] [Indexed: 04/25/2023]
Abstract
Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.
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Affiliation(s)
- Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY, USA
| | - Emily A Aherne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lora Ellenson
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ines Nikolovski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohammed Alghamdi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kara Long Roche
- Department of Surgical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ying Liu
- Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Druv Patel
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Aukerman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arfath Pasha
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Doori Rose
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pier Selenica
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Chris Fong
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marinela Capanu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jorge S Reis-Filho
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramon Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samantha Leung
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - JianJiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Ghafoor S, Becker AS, Woo S, Causa Andrieu PI, Stocker D, Gangai N, Hricak H, Vargas HA. Comparison of PI-RADS Versions 2.0 and 2.1 for MRI-based Calculation of the Prostate Volume. Acad Radiol 2021; 28:1548-1556. [PMID: 32814644 DOI: 10.1016/j.acra.2020.07.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/20/2020] [Accepted: 07/21/2020] [Indexed: 01/25/2023]
Abstract
RATIONALE AND OBJECTIVES Prostate gland volume (PGV) should be routinely included in MRI reports of the prostate. The recently updated Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 includes a change in the recommended measurement method for PGV compared to version 2.0. The purpose of this study was to evaluate the agreement of MRI-based PGV calculations with the volumetric manual slice-by-slice prostate segmentation as a reference standard using the linear measurements per PI-RADS versions 2.0 and 2.1. Furthermore, to assess inter-reader agreement for the different measurement approaches, determine the influence of an enlarged transition zone on measurement accuracy and to assess the value of the bullet formula for PGV calculation. MATERIALS AND METHODS Ninety-five consecutive treatment-naive patients undergoing prostate MRI were retrospectively analyzed. Prostates were manually contoured and segmented on axial T2-weighted images. Four different radiologists independently measured the prostate in three dimensions according to PI-RADS v2.0 and v2.1, respectively. MRI-based PGV was calculated using the ellipsoid and bullet formulas. Calculated volumes were compared to the reference manual segmentations using Wilcoxon signed-rank test. Inter-reader agreement was calculated using intraclass correlation coefficient (ICC). RESULTS Inter-reader agreement was excellent for the ellipsoid and bullet formulas using PI-RADS v2.0 (ICC 0.985 and 0.987) and v2.1 (ICC 0.990 and 0.994), respectively. The median difference from the reference standard using the ellipsoid formula derived PGV was 0.4 mL (interquartile range, -3.9 to 5.1 mL) for PI-RADS v2.0 (p = 0.393) and 2.6 mL (interquartile range, -1.6 to 7.3 mL) for v2.1 (p < 0.001) with a median difference of 2.2 mL. The bullet formula overestimated PGV by a median of 13.3 mL using PI-RADS v2.0 (p < 0.001) and 16.0 mL using v2.1 (p < 0.001). In the presence of an enlarged transition zone the PGV tended to be higher than the reference standard for PI-RADS v2.0 (median difference of 4.7 mL; p = 0.018) and for v2.1 (median difference of 5.7 mL, p < 0.001) using the ellipsoid formula. CONCLUSION Inter-reader agreement was excellent for the calculated PGV for both methods. PI-RADS v2.0 measurements with the ellipsoid formula yielded the most accurate volume estimates. The differences between PI-RADS v2.0 and v2.1 were statistically significant although small in absolute numbers but may be of relevance in specific clinical scenarios like prostate-specific antigen density calculation. These findings validate the use of the ellipsoid formula and highlight that the bullet formula should not be used for prostate volume estimation due to systematic overestimation.
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Affiliation(s)
- Soleen Ghafoor
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - Anton S Becker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Pamela I Causa Andrieu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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Do RKG, Lupton K, Causa Andrieu PI, Luthra A, Taya M, Batch K, Nguyen H, Rahurkar P, Gazit L, Nicholas K, Fong CJ, Gangai N, Schultz N, Zulkernine F, Sevilimedu V, Juluru K, Simpson A, Hricak H. Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period. Radiology 2021; 301:115-122. [PMID: 34342503 DOI: 10.1148/radiol.2021210043] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. Purpose To show the feasibility of using natural language processing (NLP) to gather accurate data from radiology reports for assessing spatial and temporal patterns of metastatic spread in a large patient cohort. Materials and Methods In this retrospective longitudinal study, consecutive patients who underwent CT from July 2009 to April 2019 and whose CT reports followed a departmental structured template were included. Three radiologists manually curated a sample of 2219 reports for the presence or absence of metastases across 13 organs; these manually curated reports were used to develop three NLP models with an 80%-20% split for training and test sets. A separate random sample of 448 manually curated reports was used for validation. Model performance was measured by accuracy, precision, and recall for each organ. The best-performing NLP model was used to generate a final database of metastatic disease across all patients. For each cancer type, statistical descriptive reports were provided by analyzing the frequencies of metastatic disease at the report and patient levels. Results In 91 665 patients (mean age ± standard deviation, 61 years ± 15; 46 939 women), 387 359 reports were labeled. The best-performing NLP model achieved accuracies from 90% to 99% across all organs. Metastases were most frequently reported in abdominopelvic (23.6% of all reports) and thoracic (17.6%) nodes, followed by lungs (14.7%), liver (13.7%), and bones (9.9%). Metastatic disease tropism is distinct among common cancers, with the most common first site being bones in prostate and breast cancers and liver among pancreatic and colorectal cancers. Conclusion Natural language processing may be applied to cancer patients' CT reports to generate a large database of metastatic phenotypes. Such a database could be combined with genomic studies and used to explore prognostic imaging phenotypes with relevance to treatment planning. © RSNA, 2021 Online supplemental material is available for this article.
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Affiliation(s)
- Richard K G Do
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Kaelan Lupton
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Pamela I Causa Andrieu
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Anisha Luthra
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Michio Taya
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Karen Batch
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Huy Nguyen
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Prachi Rahurkar
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Lior Gazit
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Kevin Nicholas
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Christopher J Fong
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Natalie Gangai
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Nikolaus Schultz
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Farhana Zulkernine
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Varadan Sevilimedu
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Krishna Juluru
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Amber Simpson
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
| | - Hedvig Hricak
- From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.)
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Causa Andrieu PI, Golia Pernicka JS, Faria E Silva Costa G, Chesnut GT, Shandu JS, Ying-Bei C, Petkovska I. Isolated urethral metastasis from appendiceal mucinous adenocarcinoma. Clin Imaging 2020; 67:68-71. [PMID: 32526660 DOI: 10.1016/j.clinimag.2020.05.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/11/2020] [Accepted: 05/28/2020] [Indexed: 10/24/2022]
Abstract
We are presenting a compelling case of a 61-year-old female with a history of appendiceal mucinous adenocarcinoma (AMA) with a new complaint of irritative lower urinary tract symptoms. Magnetic resonance imaging (MRI) showed a semi-circumferential, T2 hyperintense, rim enhancing, and lacking restricted diffusion lesion involving the urethra and infiltrating the right perineal and internal obturator muscles. The suspected differential diagnosis was urethral malignancy, based on her cancer history and MRI findings. After interdisciplinary consensus, the patient underwent excision of the lesion, and pathology was consistent with metastasis from the primary tumor. The urethra is a rare site of primary malignancy and metastatic disease. In particular, a non-contiguous metastatic disease involving the urethra is exceedingly rare. To the best of our knowledge, this is the first report of an AMA metastasizing to the urethra.
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Affiliation(s)
- Pamela I Causa Andrieu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States.
| | | | | | - Gregory T Chesnut
- Department of Urology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Jaspreet S Shandu
- Department of Urology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Chen Ying-Bei
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
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Causa Andrieu PI, Vázquez MA, Viglierchio VT, Chacón CRB. [Mayer-Rokitansky-Kuster syndrome with didelphus pattern]. Medicina (B Aires) 2020; 80:390. [PMID: 32841143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023] Open
Affiliation(s)
- Pamela I Causa Andrieu
- Servicio de Diagnóstico por Imágenes, Hospital Italiano de Buenos Aires, Argentina. E-mail:
| | | | | | - Carolina R B Chacón
- Servicio de Diagnóstico por Imágenes, Hospital Italiano de Buenos Aires, Argentina
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