Federico Mattiello

985 total citations
21 papers, 586 citations indexed

About

Federico Mattiello is a scholar working on Pathology and Forensic Medicine, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Federico Mattiello has authored 21 papers receiving a total of 586 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Pathology and Forensic Medicine, 10 papers in Oncology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Federico Mattiello's work include Lymphoma Diagnosis and Treatment (17 papers), Viral-associated cancers and disorders (7 papers) and Lung Cancer Treatments and Mutations (5 papers). Federico Mattiello is often cited by papers focused on Lymphoma Diagnosis and Treatment (17 papers), Viral-associated cancers and disorders (7 papers) and Lung Cancer Treatments and Mutations (5 papers). Federico Mattiello collaborates with scholars based in Switzerland, United States and Canada. Federico Mattiello's co-authors include Luc Bijnens, Olivier Thas, Marek Trněný, Laurie H. Sehn, Tina Nielsen, Andrea Knapp, Christopher R. Bolen, Umberto Vitolo, Neil Chua and Gila Sellam and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Federico Mattiello

21 papers receiving 573 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Federico Mattiello Switzerland	14	356	217	161	120	104	21	586
Erin Reid United States	17	535 1.5×	693 3.2×	86 0.5×	200 1.7×	151 1.5×	64	927
Rita Coutinho United Kingdom	11	374 1.1×	360 1.7×	62 0.4×	116 1.0×	57 0.5×	23	612
Etienne Mahé Canada	10	80 0.2×	86 0.4×	61 0.4×	47 0.4×	17 0.2×	36	345
Barbara Scarpati Italy	12	169 0.5×	75 0.3×	129 0.8×	203 1.7×	17 0.2×	23	561
Borden Ec United States	7	158 0.4×	163 0.8×	28 0.2×	52 0.4×	32 0.3×	13	312
Laura Susok Germany	13	104 0.3×	278 1.3×	113 0.7×	39 0.3×	27 0.3×	64	612
MM Oken United States	12	100 0.3×	246 1.1×	349 2.2×	146 1.2×	10 0.1×	15	797
D. H. Wright Uganda	13	289 0.8×	256 1.2×	46 0.3×	75 0.6×	82 0.8×	25	624
Rena R. Xian United States	11	119 0.3×	174 0.8×	114 0.7×	35 0.3×	21 0.2×	42	401
Timothy M. Kuzel United States	18	599 1.7×	234 1.1×	136 0.8×	52 0.4×	83 0.8×	34	1.2k

Countries citing papers authored by Federico Mattiello

Since Specialization

Citations

This map shows the geographic impact of Federico Mattiello's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Federico Mattiello with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Mattiello more than expected).

Fields of papers citing papers by Federico Mattiello

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Federico Mattiello. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Federico Mattiello. The network helps show where Federico Mattiello may publish in the future.

Co-authorship network of co-authors of Federico Mattiello

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Mattiello. A scholar is included among the top collaborators of Federico Mattiello based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Federico Mattiello. Federico Mattiello is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kostakoğlu, Lale, Maurizio Martelli, Laurie H. Sehn, et al.. (2023). A comparison of the prognostic performance of the Lugano 2014 and RECIL 2017 response criteria in patients with NHL from the phase III GOYA and GALLIUM trials. SHILAP Revista de lepidopterología. 4(4). 1042–1051. 1 indexed citations

Kostakoğlu, Lale, F Dalmasso, Paola Berchialla, et al.. (2022). A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA. SHILAP Revista de lepidopterología. 3(2). 406–414. 14 indexed citations

Broglio, Kristine, Lale Kostakoglu, Federico Mattiello, et al.. (2022). PET-CR as a potential surrogate endpoint in untreated DLBCL: meta-analysis and implications for clinical trial design. Leukemia & lymphoma. 63(12). 2816–2831. 3 indexed citations

Kostakoglu, Lale, Maurizio Martelli, Laurie H. Sehn, et al.. (2021). End-of-treatment PET/CT predicts PFS and OS in DLBCL after first-line treatment: results from GOYA. Blood Advances. 5(5). 1283–1290. 36 indexed citations

Kostakoglu, Lale, Federico Mattiello, Maurizio Martelli, et al.. (2021). Total metabolic tumor volume as a survival predictor for patients with diffuse large B-cell lymphoma in the GOYA study. Haematologica. 107(7). 1633–1642. 37 indexed citations

Bolen, Christopher R., Federico Mattiello, Michael Herold, et al.. (2021). Treatment dependence of prognostic gene expression signatures in de novo follicular lymphoma. Blood. 137(19). 2704–2707. 17 indexed citations

Kostakoglu, Lale, Andrew Davies, Michael Herold, et al.. (2020). Recil 2017 Criteria Demonstrated Similar Prognostic Value and Detected a Comparable Treatment Difference between Obinutuzumab- and Rituximab-Chemotherapy Compared with Cheson 2007 and Lugano 2014 Criteria in Patients with Previously Untreated Advanced-Stage Follicular Lymphoma. Blood. 136(Supplement 1). 25–26. 1 indexed citations

Mir, Farheen, Federico Mattiello, Andrew Grigg, et al.. (2020). Follicular Lymphoma Evaluation Index (FLEX): A new clinical prognostic model that is superior to existing risk scores for predicting progression‐free survival and early treatment failure after frontline immunochemotherapy. American Journal of Hematology. 95(12). 1503–1510. 32 indexed citations

Zhu, Jiawen, et al.. (2019). An efficient Bayesian platform trial design for borrowing adaptively from historical control data in lymphoma. Contemporary Clinical Trials. 89. 105890–105890. 17 indexed citations

10.

Kostakoğlu, Lale, Maurizio Martelli, Laurie H. Sehn, et al.. (2019). Complete Response Status According to RECIL 2017 Criteria Shows High Concordance with Lugano 2014 Criteria and Is Highly Prognostic for Outcome in Previously Untreated Patients with CD20-Positive Diffuse Large B-Cell Lymphoma (DLBCL). Blood. 134(Supplement_1). 489–489. 2 indexed citations

11.

Kostakoğlu, Lale, Paola Berchialla, F Dalmasso, et al.. (2019). A Prognostic Model Integrating PET-Derived Quantitative Parameters and Image Texture Analyses Using Radiomics in a Large Prospective Phase III Trial, GOYA. Blood. 134(Supplement_1). 883–883. 1 indexed citations

12.

Klánová, Magdalena, Laurie H. Sehn, Isabelle Bence‐Bruckler, et al.. (2019). Integration of cell of origin into the clinical CNS International Prognostic Index improves CNS relapse prediction in DLBCL. Blood. 133(9). 919–926. 75 indexed citations

13.

Barrington, Sally F., Judith Trotman, Deniz Şahin, et al.. (2018). Baseline PET-Derived Metabolic Tumor Volume Metrics Did Not Predict Outcomes in Follicular Lymphoma Patients Treated with First-Line Immunochemotherapy and Antibody Maintenance in the Phase III GALLIUM Study. Blood. 132(Supplement 1). 2882–2882. 13 indexed citations

14.

Rutherford, Sarah C., Michael Herold, Wolfgang Hiddemann, et al.. (2018). Bone Marrow Biopsy Impacts Response Assessment in a Minority of Patients with Follicular Lymphoma and Diffuse Large B-Cell Lymphoma Treated with Immunochemotherapy: Results from the Randomized Phase III GALLIUM and GOYA Trials. Blood. 132(Supplement 1). 1605–1605. 1 indexed citations

15.

Cheson, Bruce D., Neil Chua, Jiřı́ Mayer, et al.. (2018). Overall Survival Benefit in Patients With Rituximab-Refractory Indolent Non-Hodgkin Lymphoma Who Received Obinutuzumab Plus Bendamustine Induction and Obinutuzumab Maintenance in the GADOLIN Study. Journal of Clinical Oncology. 36(22). 2259–2266. 100 indexed citations

16.

Mir, Farheen, Andrew Grigg, Michael Herold, et al.. (2018). A Novel Prognostic Model That Is Superior to FLIPI-1 and FLIPI-2 and Integrates Clinical and Treatment Factors to Predict Progression-Free Survival and Early Treatment Failure in Patients with Follicular Lymphoma in the GALLIUM Trial. Blood. 132(Supplement 1). 2872–2872. 3 indexed citations

17.

Kostakoglu, Lale, Maurizio Martelli, Laurie H. Sehn, et al.. (2017). Baseline PET-Derived Metabolic Tumor Volume Metrics Predict Progression-Free and Overall Survival in DLBCL after First-Line Treatment: Results from the Phase 3 GOYA Study. Blood. 130(Suppl_1). 824–824. 20 indexed citations

18.

Mattiello, Federico, et al.. (2017). A broken promise: microbiome differential abundance methods do not control the false discovery rate. Briefings in Bioinformatics. 20(1). 210–221. 124 indexed citations

19.

Cheson, Bruce D., Marek Trněný, Kamal Bouabdallah, et al.. (2016). Obinutuzumab plus Bendamustine Followed by Obinutuzumab Maintenance Prolongs Overall Survival Compared with Bendamustine Alone in Patients with Rituximab-Refractory Indolent Non-Hodgkin Lymphoma: Updated Results of the GADOLIN Study. Blood. 128(22). 615–615. 20 indexed citations

20.

Mattiello, Federico, Bie Verbist, Karoline Faust, et al.. (2016). A web application for sample size and power calculation in case-control microbiome studies. Bioinformatics. 32(13). 2038–2040. 56 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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