Laura Martincich

4.9k total citations · 1 hit paper
41 papers, 1.6k citations indexed

About

Laura Martincich is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Laura Martincich has authored 41 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Cancer Research and 13 papers in Pathology and Forensic Medicine. Recurrent topics in Laura Martincich's work include MRI in cancer diagnosis (25 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Breast Cancer Treatment Studies (15 papers). Laura Martincich is often cited by papers focused on MRI in cancer diagnosis (25 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Breast Cancer Treatment Studies (15 papers). Laura Martincich collaborates with scholars based in Italy, United States and Germany. Laura Martincich's co-authors include Daniele Regge, Riccardo Ponzone, Filippo Montemurro, Massimo Aglietta, Savannah C. Partridge, Ivana Sarotto, P Sismondi, Franziska Kubatzki, Nicoletta Biglia and Paola Clauser and has published in prestigious journals such as Clinical Cancer Research, European Journal of Cancer and American Journal of Roentgenology.

In The Last Decade

Laura Martincich

39 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

2019 270 citations Pascal Baltzer, Ritse M. Mann et al. European Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Laura Martincich Italy	21	1.1k	552	422	233	227	41	1.6k
Peter R. Eby United States	25	1.7k 1.5×	533 1.0×	948 2.2×	374 1.6×	146 0.6×	53	2.4k
Carol B. Stelling United States	16	602 0.5×	456 0.8×	624 1.5×	190 0.8×	208 0.9×	36	1.3k
N D DeBruhl United States	20	1.2k 1.1×	312 0.6×	422 1.0×	133 0.6×	297 1.3×	35	1.6k
Selin Çarkaci United States	16	358 0.3×	343 0.6×	296 0.7×	240 1.0×	141 0.6×	37	914
Cecilia L. Mercado United States	19	428 0.4×	491 0.9×	858 2.0×	182 0.8×	316 1.4×	43	1.3k
Susan Koelliker United States	10	337 0.3×	416 0.8×	499 1.2×	164 0.7×	227 1.0×	13	1.1k
Mireille Van Goethem Belgium	13	768 0.7×	478 0.9×	484 1.1×	174 0.7×	117 0.5×	36	1.1k
Petrina A. Causer Canada	17	1.1k 1.0×	496 0.9×	581 1.4×	334 1.4×	160 0.7×	37	1.9k
Pietro Panizza Italy	11	432 0.4×	348 0.6×	327 0.8×	232 1.0×	71 0.3×	45	865
R. Schulz-Wendtland Germany	16	423 0.4×	434 0.8×	350 0.8×	357 1.5×	135 0.6×	65	1.2k

Countries citing papers authored by Laura Martincich

Since Specialization

Citations

This map shows the geographic impact of Laura Martincich'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 Laura Martincich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laura Martincich more than expected).

Fields of papers citing papers by Laura Martincich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Laura Martincich. 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 Laura Martincich. The network helps show where Laura Martincich may publish in the future.

Co-authorship network of co-authors of Laura Martincich

This figure shows the co-authorship network connecting the top 25 collaborators of Laura Martincich. A scholar is included among the top collaborators of Laura Martincich 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 Laura Martincich. Laura Martincich 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

Honda, Maya, Eric E. Sigmund, Denis Le Bihan, et al.. (2024). Advanced breast diffusion-weighted imaging: what are the next steps? A proposal from the EUSOBI International Breast Diffusion-weighted Imaging working group. European Radiology. 35(4). 2130–2140. 4 indexed citations

Cioni, Dania, Damiano Caruso, Clemens C. Cyran, et al.. (2022). Attitudes and perceptions of radiologists towards online (virtual) oncologic multidisciplinary team meetings during the COVID-19 pandemic—a survey of the European Society of Oncologic Imaging (ESOI). European Radiology. 33(2). 1194–1204. 8 indexed citations

Surov, Alexey, Yun‐Woo Chang, Lihua Li, et al.. (2019). Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer. 19(1). 1043–1043. 24 indexed citations

Baltzer, Pascal, Ritse M. Mann, Mami Iima, et al.. (2019). Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. European Radiology. 30(3). 1436–1450. 270 indexed citations breakdown →

Surov, Alexey, Paola Clauser, Yun‐Woo Chang, et al.. (2018). Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis. Breast Cancer Research. 20(1). 52 indexed citations

Mariscotti, Giovanna, Manuela Durando, Nehmat Houssami, et al.. (2016). Digital breast tomosynthesis as an adjunct to digital mammography for detecting and characterising invasive lobular cancers: a multi-reader study. Clinical Radiology. 71(9). 889–895. 37 indexed citations

Podo, Franca, Filippo Santoro, Giovanni Di Leo, et al.. (2015). Triple-Negative versus Non–Triple-Negative Breast Cancers in High-Risk Women: Phenotype Features and Survival from the HIBCRIT-1 MRI-Including Screening Study. Clinical Cancer Research. 22(4). 895–904. 24 indexed citations

Marinovich, M. Luke, Petra Macaskill, Les Irwig, et al.. (2015). Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer. 15(1). 662–662. 98 indexed citations

Ponzone, Riccardo, Furio Maggiorotto, Alberto Pisacane, et al.. (2015). MRI and intraoperative pathology to predict nipple–areola complex (NAC) involvement in patients undergoing NAC-sparing mastectomy. European Journal of Cancer. 51(14). 1882–1889. 58 indexed citations

10.

Vignati, A., Valentina Giannini, Luca Alessandro Carbonaro, et al.. (2014). A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker. Computer Methods and Programs in Biomedicine. 117(3). 482–488. 9 indexed citations

11.

Martincich, Laura, Ilaria Bertotto, Stefania Redana, et al.. (2012). Correlations between diffusion-weighted imaging and breast cancer biomarkers. European Radiology. 22(7). 1519–1528. 192 indexed citations

12.

Martincich, Laura, et al.. (2012). Do we really need new contrast agents for MR-mammography?. European Journal of Radiology. 81. S127–S128.

13.

Vignati, A., Valentina Giannini, Alberto Bert, et al.. (2012). A Fully Automatic Multiscale 3-Dimensional Hessian-Based Algorithm for Vessel Detection in Breast DCE-MRI. Investigative Radiology. 47(12). 705–710. 9 indexed citations

14.

Vignati, A., Valentina Giannini, M. De Luca, et al.. (2011). Performance of a fully automatic lesion detection system for breast DCE‐MRI. Journal of Magnetic Resonance Imaging. 34(6). 1341–1351. 45 indexed citations

15.

Biglia, Nicoletta, Valentina Elisabetta Bounous, Laura Martincich, et al.. (2011). Role of MRI (magnetic resonance imaging) versus conventional imaging for breast cancer presurgical staging in young women or with dense breast. European Journal of Surgical Oncology. 37(3). 199–204. 47 indexed citations

16.

Vignati, A., Valentina Giannini, Alberto Bert, et al.. (2009). A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7260. 726026–726026. 9 indexed citations

17.

Montemurro, Filippo, Laura Martincich, Ivana Sarotto, et al.. (2006). Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer. European Radiology. 17(6). 1490–1497. 49 indexed citations

18.

Martincich, Laura, Filippo Montemurro, Gaetano De Rosa, et al.. (2004). Monitoring Response to Primary Chemotherapy in Breast Cancer using Dynamic Contrast-enhanced Magnetic Resonance Imaging. Breast Cancer Research and Treatment. 83(1). 67–76. 179 indexed citations

19.

Martincich, Laura, Filippo Montemurro, Stefano Cirillo, et al.. (2003). Role of Magnetic Resonance Imaging in the prediction of tumor response in patients with locally advanced breast cancer receiving neoadjuvant chemo-therapy.. PubMed. 106(1-2). 51–8. 20 indexed citations

20.

Regge, Daniele, et al.. (1999). A case of bleeding gastric lipoma: US, CT and MR findings. European Radiology. 9(2). 256–258. 20 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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