IAS-LAB PUBLICATIONS
Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy
Authors: Garufi Giovanna; Carbognin Luisa; Sperduti Isabella; Miglietta Federica; Dieci Maria Vittoria; Mazzeo Roberta; Orlandi Armando; Gerratana Lorenzo; Palazzo Antonella; Fabi Alessandra; Paris Ida; Franco Antonio; Franceschini Gianluca; Fiorio Elena; Pilotto Sara; Guarneri Valentina; Puglisi Fabio; Conte Pierfranco; Milella Michele; Scambia Giovanni; Tortora Giampaolo; Bria Emilio
Journal: THERAPEUTIC ADVANCES IN MEDICAL ONCOLOGY
Published: 2023
DOI: 10.1177/17588359221138657
Background: Given the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features. Methods: Clinicopathological data from stage I–III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated. Results: In the training set, data from 539 patients were gathered: pCR rate was 11.3% [95% confidence interval (CI): 8.6–13.9] (luminal A-like: 5.3%, 95% CI: 1.5–9.1, and luminal B-like: 13.1%, 95% CI: 9.8–13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC): 0.69; p < 0.001). Clinical stage I–II (OR: 3.67, 95% CI: 1.75–7.71, p = 0.001), Ki67 ⩾44% (OR: 3.00, 95% CI: 1.59–5.65, p = 0.001), and progesterone receptor (PR) 1%, and Ki67 <44% to 53.3% for clinical stage I–II, PR < 1%, and Ki67 ⩾44% (accuracy: AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC: 0.7; p < 0.0001). Conclusion: The combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.
Volume: 15
Keywords: factors of response; luminal breast cancer; neoadjuvant chemotherapy; pathological complete response; predictive model;
Initial Panitumumab Plus Fluorouracil, Leucovorin, and Oxaliplatin or Plus Fluorouracil and Leucovorin in Elderly Patients with RAS and BRAF Wild-Type Metastatic Colorectal Cancer: The PANDA Trial by the GONO Foundation
Authors: Lonardi Sara; Rasola Cosimo; Lobefaro Riccardo; Rossini Daniele; Formica Vincenzo; Scartozzi Mario; Frassineti Giovanni Luca; Boscolo Giorgia; Cinieri Saverio; Di Donato Samantha; Pella Nicoletta; Bergamo Francesca; Raimondi Alessandra; Arnoldi Ermenegildo; Antonuzzo Lorenzo; Granetto Cristina; Zustovich Fable; Ronzoni Monica; Leo Silvana; Morano Federica; Loupakis Fotios; Buggin Federica; Zagonel Vittorina; Fassan Matteo; Cremolini Chiara; Boni Luca; Pietrantonio Filippo
Journal: JOURNAL OF CLINICAL ONCOLOGY
Published: 2023
DOI: 10.1200/JCO.23.00506
PURPOSETo verify whether both doublet chemotherapy with a modified schedule of fluorouracil, leucovorin, and oxaliplatin (mFOLFOX) and monochemotherapy with fluorouracil plus leucovorin (5-FU + LV) achieve satisfactory efficacy when both regimens are combined with panitumumab (PAN) as initial treatment of elderly patients with RAS/BRAF wild-type metastatic colorectal cancer (mCRC).PATIENTS AND METHODSPANDA (ClinicalTrials.gov identifier: NCT02904031) was an open-label, randomized phase II noncomparative trial in previously untreated patients age 70 years and older with unresectable RAS/BRAF wild-type mCRC. Patients were randomly assigned 1:1 to mFOLFOX + PAN (arm A) or 5-FU + LV + PAN (arm B) for up to 12 cycles, followed by PAN maintenance. The primary end point was progression-free survival (PFS). In each arm, assuming a null hypothesis of median PFS time ≤6 months and target PFS ≥9.65, 90 patients per arm were needed to achieve 90% power and 5% type I error (one-sided Brookmeyer-Crowley test).RESULTSBetween July 2016 and April 2019, 91 patients were randomly assigned to arm A and 92 to arm B. At a median follow-up of 50.0 months (IQR, 45.6-56.4), median PFS was 9.6 and 9.0 months for arm A and B, respectively (P 2 chemotherapy-related adverse events was 60% and 37%, respectively. Baseline G8 and Chemotherapy Risk Assessment Scale for High-Age Patients scores were prognostic, but they were not associated with efficacy and safety of the two arms.CONCLUSIONBoth mFOLFOX and 5-FU + LV + PAN are reasonable options as initial therapy of elderly patients with RAS/BRAF wild-type mCRC. 5-FU + LV + PAN is associated with a better safety profile.
Volume: 41 Pages: 5263-+
The role of antiplatelet therapies on incidence and mortality of hepatocellular carcinoma
Authors: Lai Quirino; De Matthaeis Nicoletta; Finotti Michele; Galati Giovanni; Marrone Giuseppe; Melandro Fabio; Morisco Filomena; Nicolini Daniele; Pravisani Riccardo; Giannini Edoardo G.
Journal: EUROPEAN JOURNAL OF CLINICAL INVESTIGATION
Published: 2023
DOI: 10.1111/eci.13870
Aim: To evaluate the impact of antiplatelet therapy (APT)on the incidence of hepatocellular carcinoma (HCC) and mortality following its treatment. Methods: A systematic literature search was performed using PubMed and Cochrane Central Register of Controlled Trials Databases. Two HCC clinical settings were explored: (i) incidence, and (ii) death after any HCC treatment. Odds ratios (OR) and 95% confidence intervals (95%CI) were calculated to compare the pooled data between patients who received or did not receive APT. Results: A total of 20 studies were identified, of whom 15 focused on HCC incidence, including 2,685,009 patients, and five on post-treatment death, including 3281 patients. APT was associated with an overall reduced risk of HCC incidence (OR: 0.63; 95%CI = 0.51–0.79; p < 0.001) as well as of post-treatment mortality (OR: 0.54; 95%CI = 0.35–0.83; p = 0.006). Conclusions: Current data suggest that APT correlated with higher HCC incidence and poor overall survival following tumour treatment.
Volume: 53
Keywords: aspirin; clopidogrel; incidence; occurrence; survival;
Author Correction: Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing (Nature Genetics, (2020), 52, 3, (331-341), 10.1038/s41588-019-0576-7)
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Journal: NATURE GENETICS
Published: 2023
DOI: 10.1038/s41588-023-01315-z
Correction to: Nature Genetics, published online 05 February 2020. In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.
Volume: 55 Pages: 1076-1076
Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers (Nature Genetics, (2020), 52, 3, (342-352), 10.1038/s41588-019-0557-x)
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Journal: NATURE GENETICS
Published: 2023
DOI: 10.1038/s41588-023-01317-x
Correction to: Nature Genetics, published online 05 February 2020. In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.
Volume: 55 Pages: 1078-1078
Euclid preparation: XXVI. the Euclid Morphology Challenge: Towards structural parameters for billions of galaxies
Authors: Non assegn; AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; CIW-4665-2022; IWF-0947-2023; GBV-7145-2022; DVP-3997-2022; IVA-4275-2023; EDC-3693-2022; K-9464-2019; B-4348-2013; GDW-2905-2022; GBN-3512-2022; GWA-7849-2022; CTE-6491-2022; GRO-8426-2022; B-9902-2008; FZT-3187-2022; ABC-3716-2021; DWU-8294-2022; CUC-4357-2022; JWP-3376-2024; H-5733-2014; DQT-2192-2022; PCO-3236-2025; EPI-1133-2022; Y-9126-2019; DWO-2405-2022; KLD-3528-2024; DWY-3410-2022; A-5940-2015; GBD-1861-2022; DVE-7652-2022; H-4394-2019; EJM-8740-2022; FZO-1254-2022; CDR-2303-2022; DVO-2403-2022; C-4378-2014; GBF-1843-2022; EKV-4052-2022; L-8385-2017; J-2774-2019; M-2616-2015; AAG-7753-2020; GBO-0318-2022; E-2727-2014; L-8237-2014; E-8021-2017; B-4650-2017; EQA-1830-2022; GLW-3038-2022; H-8587-2015; DVC-6323-2022; B-4928-2015; GBN-8818-2022; LEQ-1557-2024; FZL-7353-2022; A-2693-2010; PCA-2324-2025; HKB-2933-2023; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; J-3686-2012; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; ITP-9423-2023; GAZ-3876-2022; GBB-1832-2022; DWB-6758-2022; ABF-7029-2021; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; FBF-5584-2022; ABF-1380-2020; FBV-0790-2022; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; DWQ-9372-2022; DXH-0671-2022; FFG-2233-2022; DAV-9216-2022; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; FIV-3763-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; AAX-3485-2021; D-1300-2016; AAA-1489-2019; GNG-7078-2022; DFC-8070-2022; AAB-4321-2020; KEK-6332-2024; AAW-4410-2021; DWT-4779-2022; DZP-5216-2022; FNB-0821-2022; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; IVG-7504-2023; FSY-2184-2022; ABC-8644-2021; DNY-0415-2022; AAR-4345-2020; K-4114-2015; OON-3882-2025; AFE-8548-2022; DXL-4304-2022; FVF-5606-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; DPD-7597-2022; IZJ-2041-2023; GBV-4959-2022; I-8498-2012; J-5067-2012; NES-1075-2025; JAE-9097-2023; L-8068-2014; CFK-4637-2022; Q-2220-2015; T-7378-2018; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; Q-6715-2019; ABD-6783-2021; EAZ-0566-2022; EIA-6036-2022; FYJ-9637-2022; O-9369-2015; HTG-8587-2023; CMV-6954-2022; GBY-6621-2022; GAU-7672-2022; B-8712-2017; FNC-4379-2022; FVK-3262-2022; O-9396-2015; CEY-5520-2022; AAZ-4907-2020; IUS-1317-2023; AAO-6325-2021; KTI-3074-2024; B-3004-2019; DTO-7937-2022; O-9391-2015; KDL-3231-2024; JFJ-3489-2023; OZD-6988-2025; KUC-6512-2024; CMH-0439-2022; AGZ-3259-2022; PDR-7717-2025; ERD-3189-2022; CNT-5485-2022; ISC-4027-2023; AAW-1061-2020; COQ-7299-2022; CPC-6980-2022; DTP-1685-2022; CRZ-8120-2022; C-2920-2017; PDR-5897-2025; ABB-8257-2020; JOZ-7589-2023; S-8590-2017; AAK-4578-2020; FYO-6165-2022; GZL-0460-2022; ITW-2356-2023; DWS-1040-2022; HRX-7202-2023; IZP-8032-2023; FZY-7746-2022; DWL-3001-2022; ABA-3922-2020; HUJ-7899-2023; U-7309-2018; DZP-0372-2022; FKW-0156-2022; Z-3406-2019; AAI-1245-2021; AAN-7016-2021; LXV-7382-2024; DYG-8551-2022; C-3470-2011; JCV-3612-2023; GCY-0967-2022; AAY-8788-2021; IWG-9653-2023; GEF-7978-2022; FXG-6905-2022; FXS-9180-2022; GBG-9412-2022; IYS-3498-2023; GCB-1754-2022; DXU-7894-2022; LXA-1722-2024; A-9058-2016; EDW-5764-2022; AAC-2083-2020; DYK-4428-2022; DXW-9679-2022; NKX-2133-2025; 57225065454; 55868798300; 23050749700; 14832846900; 24439181000; 57194413215; 36195346900; 7003910265; 57189300153; 58148393500; 56216916000; 24279354600; 57221803833; 7005656061; 8719314400; 57217712130; 7801607411; 57215682687; 57216343493; 15754453800; 57194419532; 36238621200; 6602458029; 36237905600; 7004293616; 36627225700; 57193558463; 57219746647; 55541304900; 35494536400; 55929371000; 6701390827; 14629998500; 8651648800; 24830335500; 24482926400; 7102960752; 6506892241; 6701447926; 7004168457; 7004279376; 35117442400; 56592859600; 8316050500; 57193414472; 7004614794; 54924573500; 56260193000; 55959642400; 37121732700; 6507398813; 55757270100; 56018146100; 8856476200; 7004529134; 26663174300; 6603213706; 57212263363; 6506323808; 55978579700; 6601991850; 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56176939800; 57225389323; 55543336500; 9244606800; 12809267200; 56403356600; 26326923900; 7102846243; 56512377200; 9333441800; 57220414927; 56818885600; 7003804366; 7004185737; 7006764136; 56134803400; 6603819488; 9337037600; 7103030457; 57090221700; 55948641800; 35112881300; 23485171100; 24069757200; 35072386500; 6602521535; 57199061795; 54402554200; 6602348000; 24461026200; 56181792800; 55420010100; 7004144883; 7003645652; 57206536839; 15131601400; 8833942900; 7006022077; 35239938700; 58148943200; 23485209600; 57218097629; 55885669700; 26642611400; 57210924350; 6508080858; 6701309093; 6603292899; 57815020000; 56463558800; 55779479900; 23393165800; 7006221760; 35422761600; 42262265600; 6603351766; 7003762062; 7101903552; 57203270249; 16031797900; 35113641200; 57220131178; 36495525900; 56881732300; 57219119015; 8833942000; 6506381727; 7005050491; 7004680980; 6701718244; 6506892358; 57374950500; 56286395400; 7003963996; 57217789465; 7404952697
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202245042
The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline of the Organisational Unit MER of the Euclid Science Ground Segment, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper focusses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes, DeepLeGATo, Galapagos-2, Morfometryka, ProFit and SourceXtractor++, on a sample of about 1.5 million simulated galaxies (350 000 above 5s) resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (< 10% scatter on ideal Sérsic simulations) down to an apparent magnitude of about IE = 23 in one component and IE = 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5, respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the official Euclid Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.
Volume: 671
Keywords: Cosmology: observations; Galaxies: evolution; Galaxies: fundamental parameters; Methods: data analysis;
Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition (Nature Genetics, (2020), 52, 3, (306-319), 10.1038/s41588-019-0562-0)
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Journal: NATURE GENETICS
Published: 2023
DOI: 10.1038/s41588-023-01319-9
Correction to: Nature Genetics, published online 05 February 2020. In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper. Additional affiliations have been added for author Kathleen H. Burns (McKusick-Nathans Institute of Genetic Medicine, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, USA) and author Guillaume Bourque (Canadian Center for Computational Genomics, McGill University, Montreal, Quebec, Canada).
Volume: 55 Pages: 1080-1080
Author Correction: Patterns of somatic structural variation in human cancer genomes (Nature, (2020), 578, 7793, (112-121), 10.1038/s41586-019-1913-9)
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Journal: NATURE
Published: 2023
DOI: 10.1038/s41586-022-05597-x
In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional minor corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.
Volume: 614 Pages: E38-E38
Author Correction: Analyses of non-coding somatic drivers in 2,658 cancer whole genomes (Nature, (2020), 578, 7793, (102-111), 10.1038/s41586-020-1965-x)
Authors: AREA MIN. 06 - Scienze mediche; NATURE###0028-0836; Goal 3: Good health and well-being###25122; GAW-7722-2022; IGV-0844-2023; F-2267-2016; GKQ-0335-2022; FVN-2824-2022; GHL-6006-2022; CVX-3737-2022; CTE-8718-2022; HGM-3834-2022; MRB-3963-2025; AAT-1226-2021; GEH-2398-2022; DWU-4099-2022; HQR-3924-2023; B-3777-2017; IGF-9394-2023; JGU-5477-2023; H-5859-2011; G-6261-2010; JGD-6876-2023; DVJ-2216-2022; CEU-1922-2022; GCZ-3563-2022; DVZ-4374-2022; ENX-7020-2022; IYG-6783-2023; DWG-2161-2022; CHT-7428-2022; EPB-5354-2022; ESF-7544-2022; B-3352-2014; B-7801-2009; DTM-9505-2022; HVO-2687-2023; MVQ-4462-2025; GCI-7047-2022; LLQ-9112-2024; GDX-1122-2022; ABG-4704-2021; FZY-3673-2022; ABB-8343-2020; HIR-4014-2022; HQF-9907-2023; M-2667-2013; IME-8582-2023; IVF-5829-2023; HQR-4379-2023; GAF-2079-2022; MYW-6714-2025; V-1738-2019; DEI-9877-2022; ABD-5852-2021; DVF-7129-2022; JOP-2884-2023; DQI-0934-2022; E-9821-2013; OVI-7023-2025; FVR-5229-2022; DVB-7032-2022; FQH-6752-2022; M-5283-2015; 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Journal: NATURE
Published: 2023
DOI: 10.1038/s41586-022-05599-9
Correction to: Nature Published online 5 February 2020 In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional minor corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper. An additional affiliation has been added for Husen M. Umer (Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden). The affiliation for Erik Larsson has been changed from Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA to Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
Volume: 614 Pages: E40-E40
Euclid preparation: XXII. Selection of quiescent galaxies from mock photometry using machine learning
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; M-4118-2013; HNI-9120-2023; IWH-1605-2023; O-9495-2015; KLD-3528-2024; DWH-1491-2022; H-4394-2019; EAZ-0566-2022; M-4206-2013; Z-3026-2019; M-2616-2015; AAW-4410-2021; FZO-1254-2022; CDR-2303-2022; C-4378-2014; GBF-1843-2022; DVB-2560-2022; L-8385-2017; J-2774-2019; AAG-7753-2020; GBO-0318-2022; E-2727-2014; L-8237-2014; E-8021-2017; IVA-4275-2023; B-4650-2017; IRQ-6937-2023; GLW-3038-2022; H-8587-2015; B-4348-2013; DVC-6323-2022; B-4928-2015; GBN-8818-2022; PGG-2427-2026; FZL-7353-2022; A-2693-2010; PCA-2324-2025; GBB-1963-2022; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; J-3686-2012; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; ITP-9423-2023; GAZ-3876-2022; JGR-4365-2023; GBB-1832-2022; DWB-6758-2022; GQH-6424-2022; ABF-7029-2021; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; FBV-0790-2022; CTZ-4163-2022; DWQ-9372-2022; OOP-8239-2025; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; FGD-1080-2022; DAV-9216-2022; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; FIV-3763-2022; DWC-8789-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; AAX-3485-2021; D-1300-2016; FNO-5530-2022; AAA-1489-2019; GNG-7078-2022; DFC-8070-2022; AAB-4321-2020; DVP-3997-2022; KEK-6332-2024; DWT-4779-2022; DZP-5216-2022; DKR-3047-2022; FNB-0821-2022; ABB-2322-2020; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; GBD-7573-2022; MTQ-2344-2025; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; K-4114-2015; OON-3882-2025; AFE-8548-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; KCY-7756-2024; DPD-7597-2022; OIQ-4966-2025; IZJ-2041-2023; GBV-4959-2022; I-8498-2012; J-5067-2012; EAA-4768-2022; L-8068-2014; CFK-7257-2022; CFK-4637-2022; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; HNI-8187-2023; GCA-5567-2022; Q-6715-2019; ABD-6783-2021; DZU-8266-2022; EIA-6036-2022; FYJ-9637-2022; O-9369-2015; GDW-2905-2022; GVS-7124-2022; GBY-6621-2022; FLK-4707-2022; B-8712-2017; FNC-4379-2022; FNU-9642-2022; GFA-3443-2022; P-2194-2018; AAQ-4773-2020; O-9396-2015; CEY-5520-2022; DVH-0096-2022; AAO-6325-2021; PDD-1794-2025; KTI-3074-2024; CHT-0596-2022; DTO-7937-2022; EPI-1133-2022; O-9391-2015; KDL-3231-2024; JFJ-3489-2023; OZD-6988-2025; HTG-8587-2023; KUC-6512-2024; CMH-0439-2022; AGZ-3259-2022; EQT-2114-2022; PDR-7717-2025; ERD-3189-2022; IDQ-0489-2023; ISC-4027-2023; AAW-1061-2020; COQ-7299-2022; CPC-6980-2022; DUJ-9002-2022; CRZ-8120-2022; C-2920-2017; PDR-5897-2025; DWN-8747-2022; ABB-8257-2020; S-8590-2017; GBV-7145-2022; GZL-0460-2022; ITW-2356-2023; FEB-7061-2022; DWS-1040-2022; HRX-7202-2023; GAU-7672-2022; FLM-0394-2022; ABA-3922-2020; HUJ-7899-2023; NNN-1702-2025; DZP-0372-2022; FMO-3603-2022; D-1237-2017; AAI-1245-2021; LXV-7382-2024; DYG-8551-2022; FTV-6637-2022; JUZ-7207-2023; C-3470-2011; JCV-3612-2023; GCY-0967-2022; GEF-7978-2022; FXG-6905-2022; GBG-9412-2022; GDD-3627-2022; FVK-3262-2022; IYS-3498-2023; DXU-7894-2022; LXA-1722-2024; ECF-2024-2022; A-9058-2016; DYK-4428-2022; KBQ-5464-2024; JOZ-7589-2023; 56276774200; 56426999100; 57330640700; 6602409206; 36627225700; 8554125200; 55929371000; 8042894900; 6701323452; 14632583100; 7004279376; 35299820900; 14629998500; 8651648800; 24482926400; 7102960752; 6602293713; 6701447926; 7004168457; 35117442400; 56592859600; 8316050500; 57193414472; 7004614794; 24439181000; 54924573500; 56260193000; 55959642400; 37121732700; 7003910265; 6507398813; 55757270100; 56018146100; 8856476200; 7004529134; 26663174300; 6603213706; 6701702242; 6506323808; 55978579700; 6601991850; 6602678698; 57200514857; 9639653200; 6506341877; 56592156500; 24173378000; 57203599140; 14630273900; 56261365400; 8724245000; 36657273100; 8527480900; 9270789600; 14008117700; 9337191600; 6603519641; 7202555066; 16024707000; 55539553700; 56216916000; 35227493200; 37123976000; 6701865592; 8899520500; 6506922416; 7102120605; 6603602446; 6603205767; 6603770482; 36195926600; 10239419900; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202244307
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have ‘sparsity awareness’, such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the ‘pseudo-labelling’ semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of âÂ? ²0.03 and a fraction of catastrophic outliers of âÂ? ²0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE YE, JE HE and u IE, IE JE colour-colour methods, with improvements in completeness and the F1-score (the harmonic mean of precision and recall) of up to a factor of 2.
Volume: 671
Keywords: Galaxies: evolution; Galaxies: general; Galaxies: high-redshift; Galaxies: photometry; Methods: statistical;