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Tareco Bucho TM, Tissier RLM, Groot Lipman KBW, Bodalal Z, Delli Pizzi A, Nguyen-Kim TDL, Beets-Tan RGH, Trebeschi S. How Does Target Lesion Selection Affect RECIST? A Computer Simulation Study. Invest Radiol 2024; 59:465-471. [PMID: 37921780 DOI: 10.1097/rli.0000000000001045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
OBJECTIVES Response Evaluation Criteria in Solid Tumors (RECIST) is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden (TTB) during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection. MATERIALS AND METHODS Disagreement among readers and the disagreement between individual reader measurements and TTB were analyzed as a function of the total number of lesions, affected organs, and lesion growth. RESULTS Disagreement rises when the number of lesions increases, when lesions are concentrated on a few organs, and when lesion growth borders the thresholds of progressive disease and partial response. There is an intrinsic methodological error in the estimation of TTB via RECIST 1.1, which depends on the number of lesions and their distributions. For example, for a fixed number of lesions at 5 and 15, distributed over a maximum of 4 organs, the error rates are observed to be 7.8% and 17.3%, respectively. CONCLUSIONS Our results demonstrate that RECIST can deliver an accurate estimate of TTB in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions," which introduces a bias that makes it hardly possible to perform an accurate estimate of the TTB. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.
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Affiliation(s)
- Teresa M Tareco Bucho
- From the Radiology Department (T.T.B., K.G.L., Z.B., T.D.L.N.-K., R.B.-T., S.T.), Biostatistics Unit (R.T.), and Thoracic Oncology (K.G.L.), Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (T.T.B., K.G.L., Z.B., R.B.-T., S.T.); Institute for Advanced Biomedical Technologies, Gabriele d'Annunzio University of Chieti-Pescara, Italy (A.D.P.); Department of Innovative Technologies in Medicine and Dentistry, Gabriele d'Annunzio University of Chieti-Pescara, Italy (A.D.P.); Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland (T.D.L.N.-K.); Institute of Radiology and Nuclear Medicine, Stadtspital Zürich, Zurich, Switzerland (T.D.L.N.-K.); and Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark (R.B.-T.)
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Hoffmann E, Masthoff M, Kunz WG, Seidensticker M, Bobe S, Gerwing M, Berdel WE, Schliemann C, Faber C, Wildgruber M. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol 2024; 21:428-448. [PMID: 38641651 DOI: 10.1038/s41571-024-00891-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 04/21/2024]
Abstract
Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Bobe
- Gerhard Domagk Institute of Pathology, University Hospital Münster, Münster, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | | | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
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Ocaña-Tienda B, Pérez-Beteta J, Romero-Rosales JA, Asenjo B, Ortiz de Mendivil A, Pérez Romasanta LA, Albillo Labarra JD, Nagib F, Vidal Denis M, Luque B, Arana E, Pérez-García VM. Volumetric analysis: Rethinking brain metastases response assessment. Neurooncol Adv 2024; 6:vdad161. [PMID: 38187872 PMCID: PMC10771272 DOI: 10.1093/noajnl/vdad161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024] Open
Abstract
Background The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) criteria are the gold standard for assessing brain metastases (BMs) treatment response. However, they are limited by their reliance on 1D, despite the routine use of high-resolution T1-weighted MRI scans for BMs, which allows for 3D measurements. Our study aimed to investigate whether volumetric measurements could improve the response assessment in patients with BMs. Methods We retrospectively evaluated a dataset comprising 783 BMs and analyzed the response of 185 of them from 132 patients who underwent stereotactic radiotherapy between 2007 and 2021 at 5 hospitals. We used T1-weighted MRIs to compute the volume of the lesions. For the volumetric criteria, progressive disease was defined as at least a 30% increase in volume, and partial response was characterized by a 20% volume reduction. Results Our study showed that the proposed volumetric criteria outperformed the RANO-BM criteria in several aspects: (1) Evaluating every lesion, while RANO-BM failed to evaluate 9.2% of them. (2) Classifying response effectively in 140 lesions, compared to only 72 lesions classified by RANO-BM. (3) Identifying BM recurrences a median of 3.3 months earlier than RANO-BM criteria. Conclusions Our study demonstrates the superiority of volumetric criteria in improving the response assessment of BMs compared to the RANO-BM criteria. Our proposed criteria allow for evaluation of every lesion, regardless of its size or shape, better classification, and enable earlier identification of progressive disease. Volumetric criteria provide a standardized, reliable, and objective tool for assessing treatment response.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | | | - Beatriz Asenjo
- Department of Radiology, Hospital Regional Universitario Carlos Haya, Málaga, Spain
| | - Ana Ortiz de Mendivil
- Department of Radiology, Sanchinarro University Hospital, HM Hospitales, Madrid, Spain
| | | | | | - Fátima Nagib
- Department of Radiology, Hospital Regional Universitario Carlos Haya, Málaga, Spain
| | - María Vidal Denis
- Department of Radiology, Hospital Regional Universitario Carlos Haya, Málaga, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
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Iannessi A, Beaumont H. Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us? Front Oncol 2023; 13:988784. [PMID: 37007064 PMCID: PMC10060958 DOI: 10.3389/fonc.2023.988784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 02/03/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundIn clinical trials with imaging, Blinded Independent Central Review (BICR) with double reads ensures data blinding and reduces bias in drug evaluations. As double reads can cause discrepancies, evaluations require close monitoring which substantially increases clinical trial costs. We sought to document the variability of double reads at baseline, and variabilities across individual readers and lung trials.Material and methodsWe retrospectively analyzed data from five BICR clinical trials evaluating 1720 lung cancer patients treated with immunotherapy or targeted therapy. Fifteen radiologists were involved. The variability was analyzed using a set of 71 features derived from tumor selection, measurements, and disease location. We selected a subset of readers that evaluated ≥50 patients in ≥two trials, to compare individual reader’s selections. Finally, we evaluated inter-trial homogeneity using a subset of patients for whom both readers assessed the exact same disease locations. Significance level was 0.05. Multiple pair-wise comparisons of continuous variables and proportions were performed using one-way ANOVA and Marascuilo procedure, respectively.ResultsAcross trials, on average per patient, target lesion (TL) number ranged 1.9 to 3.0, sum of tumor diameter (SOD) 57.1 to 91.9 mm. MeanSOD=83.7 mm. In four trials, MeanSOD of double reads was significantly different. Less than 10% of patients had TLs selected in completely different organs and 43.5% had at least one selected in different organs. Discrepancies in disease locations happened mainly in lymph nodes (20.1%) and bones (12.2%). Discrepancies in measurable disease happened mainly in lung (19.6%). Between individual readers, the MeanSOD and disease selection were significantly different (p<0.001). In inter-trials comparisons, on average per patient, the number of selected TLs ranged 2.1 to 2.8, MeanSOD 61.0 to 92.4 mm. Trials were significantly different in MeanSOD (p<0.0001) and average number of selected TLs (p=0.007). The proportion of patients having one of the top diseases was significantly different only between two trials for lung. Significant differences were observed for all other disease locations (p<0.05).ConclusionsWe found significant double read variabilities at baseline, evidence of reading patterns and a means to compare trials. Clinical trial reliability is influenced by the interplay of readers, patients and trial design.
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Ng TSC, Hu H, Kronister S, Lee C, Li R, Gerosa L, Stopka SA, Burgenske DM, Khurana I, Regan MS, Vallabhaneni S, Putta N, Scott E, Matvey D, Giobbie-Hurder A, Kohler RH, Sarkaria JN, Parangi S, Sorger PK, Agar NYR, Jacene HA, Sullivan RJ, Buchbinder E, Mikula H, Weissleder R, Miller MA. Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy. SCIENCE ADVANCES 2022; 8:eabl6339. [PMID: 35486732 PMCID: PMC9054019 DOI: 10.1126/sciadv.abl6339] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BRAF-targeted kinase inhibitors (KIs) are used to treat malignancies including BRAF-mutant non-small cell lung cancer, colorectal cancer, anaplastic thyroid cancer, and, most prominently, melanoma. However, KI selection criteria in patients remain unclear, as are pharmacokinetic/pharmacodynamic (PK/PD) mechanisms that may limit context-dependent efficacy and differentiate related drugs. To address this issue, we imaged mouse models of BRAF-mutant cancers, fluorescent KI tracers, and unlabeled drug to calibrate in silico spatial PK/PD models. Results indicated that drug lipophilicity, plasma clearance, faster target dissociation, and, in particular, high albumin binding could limit dabrafenib action in visceral metastases compared to other KIs. This correlated with retrospective clinical observations. Computational modeling identified a timed strategy for combining dabrafenib and encorafenib to better sustain BRAF inhibition, which showed enhanced efficacy in mice. This study thus offers principles of spatial drug action that may help guide drug development, KI selection, and combination.
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Affiliation(s)
- Thomas S. C. Ng
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Huiyu Hu
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Stefan Kronister
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Institute of Applied Synthetic Chemistry, Technische Universität Wien, Vienna, Austria
| | - Chanseo Lee
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Ran Li
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Luca Gerosa
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sylwia A. Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Ishaan Khurana
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Michael S. Regan
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sreeram Vallabhaneni
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Niharika Putta
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Ella Scott
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Dylan Matvey
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Anita Giobbie-Hurder
- Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Rainer H. Kohler
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Jann N. Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Sareh Parangi
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Nathalie Y. R. Agar
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Heather A. Jacene
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Ryan J. Sullivan
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Hannes Mikula
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Institute of Applied Synthetic Chemistry, Technische Universität Wien, Vienna, Austria
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Miles A. Miller
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Corresponding author.
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Krasovitsky M, Lee YC, Sim HW, Chawla T, Moore H, Moses D, Baker L, Mandel C, Kielar A, Hartery A, O'Malley M, Friedlander M, Oza AM, Wang L, Lheureux S, Wilson M. Interobserver and intraobserver variability of RECIST assessment in ovarian cancer. Int J Gynecol Cancer 2022; 32:656-661. [PMID: 35379690 DOI: 10.1136/ijgc-2021-003319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES Measurement of Response Evaluation Criteria In Solid Tumors (RECIST) relies on reproducible unidimensional tumor measurements. This study assessed intraobserver and interobserver variability of target lesion selection and measurement, according to RECIST version 1.1 in patients with ovarian cancer. METHODS Eight international radiologists independently viewed 47 images demonstrating malignant lesions in patients with ovarian cancer and selected and measured lesions according to RECIST V.1.1 criteria. Thirteen images were viewed twice. Interobserver variability of selection and measurement were calculated for all images. Intraobserver variability of selection and measurement were calculated for images viewed twice. Lesions were classified according to their anatomical site as pulmonary, hepatic, pelvic mass, peritoneal, lymph nodal, or other. Lesion selection variability was assessed by calculating the reproducibility rate. Lesion measurement variability was assessed with the intra-class correlation coefficient. RESULTS From 47 images, 82 distinct lesions were identified. For lesion selection, the interobserver and intraobserver reproducibility rates were high, at 0.91 and 0.93, respectively. Interobserver selection reproducibility was highest (reproducibility rate 1) for pelvic mass and other lesions. Intraobserver selection reproducibility was highest (reproducibility rate 1) for pelvic mass, hepatic, nodal, and other lesions. Selection reproducibility was lowest for peritoneal lesions (interobserver reproducibility rate 0.76 and intraobserver reproducibility rate 0.69). For lesion measurement, the overall interobserver and intraobserver intraclass correlation coefficients showed very good concordance of 0.84 and 0.94, respectively. Interobserver intraclass correlation coefficient showed very good concordance for hepatic, pulmonary, peritoneal, and other lesions, and ranged from 0.84 to 0.97, but only moderate concordance for lymph node lesions (0.58). Intraobserver intraclass correlation coefficient showed very good concordance for all lesions, ranging from 0.82 to 0.99. In total, 85% of total measurement variability resulted from interobserver measurement difference. CONCLUSIONS Our study showed that while selection and measurement concordance were high, there was significant interobserver and intraobserver variability. Most resulted from interobserver variability. Compared with other lesions, peritoneal lesions had the lowest selection reproducibility, and lymph node lesions had the lowest measurement concordance. These factors need consideration to improve response assessment, especially as progression free survival remains the most common endpoint in phase III trials.
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Affiliation(s)
- Michael Krasovitsky
- Medical Oncology, Prince of Wales Hospital and Royal Hospital for Women, Randwick, New South Wales, Australia.,Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Yeh Chen Lee
- Medical Oncology, Prince of Wales Hospital and Royal Hospital for Women, Randwick, New South Wales, Australia.,Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.,NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Hao-Wen Sim
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.,NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Tanya Chawla
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Helen Moore
- Department of Radiology, Auckland City Hospital, Auckland, Hospital, New Zealand
| | - Daniel Moses
- Department of Radiology, Prince of Wales Hospital and Royal Hospital for Women, Randwick, New South Wales, Australia
| | - Luke Baker
- Westmead Hospital, Westmead, New South Wales, Australia
| | - Catherine Mandel
- Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ania Kielar
- University of Toronto, Toronto, Ontario, Canada.,University of Ottawa, Ottawa, Ontario, Canada
| | - Angus Hartery
- Memorial University of Newfoundland, St John's, Newfoundland, Canada
| | - Martin O'Malley
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Michael Friedlander
- Medical Oncology, Prince of Wales Hospital and Royal Hospital for Women, Randwick, New South Wales, Australia.,Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Amit M Oza
- University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
| | - Lisa Wang
- University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
| | - Stephanie Lheureux
- University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
| | - Michelle Wilson
- Cancer and Blood, Auckland City Hospital, Auckland, New Zealand
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7
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Fehrenbach U, Xin S, Hartenstein A, Auer TA, Dräger F, Froböse K, Jann H, Mogl M, Amthauer H, Geisel D, Denecke T, Wiedenmann B, Penzkofer T. Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making. Cancers (Basel) 2021; 13:2726. [PMID: 34072865 PMCID: PMC8199286 DOI: 10.3390/cancers13112726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/22/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). METHODS Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). RESULTS Internal validation of the model's accuracy showed a high overlap for NELM and livers (Matthew's correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). CONCLUSION The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model's measurements correlated well with MCC's evaluation of therapeutic response.
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Affiliation(s)
- Uli Fehrenbach
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Siyi Xin
- Division of Gastroenterology, Medical Department, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.X.); (H.J.); (B.W.)
| | - Alexander Hartenstein
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
- Bayer AG, 13353 Berlin, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
- Berlin Institute of Health, 10178 Berlin, Germany
| | - Franziska Dräger
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Konrad Froböse
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Henning Jann
- Division of Gastroenterology, Medical Department, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.X.); (H.J.); (B.W.)
| | - Martina Mogl
- Department of Surgery Campus Charité Mitte/Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany;
| | - Holger Amthauer
- Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany;
| | - Dominik Geisel
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, 04103 Leipzig, Germany;
| | - Bertram Wiedenmann
- Division of Gastroenterology, Medical Department, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.X.); (H.J.); (B.W.)
| | - Tobias Penzkofer
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
- Berlin Institute of Health, 10178 Berlin, Germany
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8
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Giannini V, Rosati S, Defeudis A, Balestra G, Vassallo L, Cappello G, Mazzetti S, De Mattia C, Rizzetto F, Torresin A, Sartore-Bianchi A, Siena S, Vanzulli A, Leone F, Zagonel V, Marsoni S, Regge D. Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy. Int J Cancer 2020; 147:3215-3223. [PMID: 32875550 DOI: 10.1002/ijc.33271] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/06/2020] [Accepted: 08/12/2020] [Indexed: 12/20/2022]
Abstract
The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.
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Affiliation(s)
- Valentina Giannini
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Samanta Rosati
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Arianna Defeudis
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Gabriella Balestra
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | | | - Giovanni Cappello
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Physics, Università degli Studi di Milano, Milan, Italy
| | - Andrea Sartore-Bianchi
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Salvatore Siena
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco Leone
- Medical Oncology, ASL Biella, Biella, Italy
- Department of Oncology, University of Turin, Turin, Italy
| | - Vittorina Zagonel
- Medical Oncology Unit 1, Istituto Oncologico Veneto-IRCCS, Padova, Italy
| | - Silvia Marsoni
- Precision Oncology, IFOM-The FIRC Institute of Molecular Oncology, Milan, Italy
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
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