Literature DB >> 26736376

Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features.

Nicola Dinapoli, Anna Rita Alitto, Mauro Vallati, Roberto Gatta, Rosa Autorino, Luca Boldrini, Andrea Damiani, Vincenzo Valentini.   

Abstract

Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease -which is highly localised and lead to significant social costs- and the large number of images that are available for each patient. At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom.

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Year:  2015        PMID: 26736376     DOI: 10.1109/EMBC.2015.7318476

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  20 in total

1.  Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer.

Authors:  Davide Cusumano; Nicola Dinapoli; Luca Boldrini; Giuditta Chiloiro; Roberto Gatta; Carlotta Masciocchi; Jacopo Lenkowicz; Calogero Casà; Andrea Damiani; Luigi Azario; Johan Van Soest; Andre Dekker; Philippe Lambin; Marco De Spirito; Vincenzo Valentini
Journal:  Radiol Med       Date:  2017-12-11       Impact factor: 3.469

Review 2.  Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives.

Authors:  Bruno Fionda; Luca Boldrini; Andrea D'Aviero; Valentina Lancellotta; Maria Antonietta Gambacorta; György Kovács; Stefano Patarnello; Vincenzo Valentini; Luca Tagliaferri
Journal:  J Contemp Brachytherapy       Date:  2020-10-30

Review 3.  Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers.

Authors:  Maryam Gul; Kimberley-Jane C Bonjoc; David Gorlin; Chi Wah Wong; Amirah Salem; Vincent La; Aleksandr Filippov; Abbas Chaudhry; Muhammad H Imam; Ammar A Chaudhry
Journal:  Front Oncol       Date:  2021-07-07       Impact factor: 6.244

4.  Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma.

Authors:  Zhong-Guo Liang; Hong Qi Tan; Fan Zhang; Lloyd Kuan Rui Tan; Li Lin; Jacopo Lenkowicz; Haitao Wang; Enya Hui Wen Ong; Grace Kusumawidjaja; Jun Hao Phua; Soon Ann Gan; Sze Yarn Sin; Yan Yee Ng; Terence Wee Tan; Yoke Lim Soong; Kam Weng Fong; Sung Yong Park; Khee-Chee Soo; Joseph Tien Wee; Xiao-Dong Zhu; Vincenzo Valentini; Luca Boldrini; Ying Sun; Melvin Lee Chua
Journal:  Br J Radiol       Date:  2019-08-27       Impact factor: 3.039

5.  Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach.

Authors:  Luca Boldrini; Davide Cusumano; Giuditta Chiloiro; Calogero Casà; Carlotta Masciocchi; Jacopo Lenkowicz; Francesco Cellini; Nicola Dinapoli; Luigi Azario; Stefania Teodoli; Maria Antonietta Gambacorta; Marco De Spirito; Vincenzo Valentini
Journal:  Radiol Med       Date:  2018-10-29       Impact factor: 3.469

6.  Hybrid Tri-Co-60 MRI radiotherapy for locally advanced rectal cancer: An in silico evaluation.

Authors:  Luca Boldrini; Elisa Placidi; Nicola Dinapoli; Luigi Azario; Francesco Cellini; Mariangela Massaccesi; Silvia Chiesa; Maria Antonietta Gambacorta; Gian Carlo Mattiucci; Danila Piccari; Stefania Teodoli; Marco De Spirito; Vincenzo Valentini
Journal:  Tech Innov Patient Support Radiat Oncol       Date:  2018-03-31

7.  Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform.

Authors:  Isabella Fornacon-Wood; Hitesh Mistry; Christoph J Ackermann; Fiona Blackhall; Andrew McPartlin; Corinne Faivre-Finn; Gareth J Price; James P B O'Connor
Journal:  Eur Radiol       Date:  2020-06-01       Impact factor: 5.315

8.  Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study).

Authors:  Camilla Nero; Francesca Ciccarone; Luca Boldrini; Jacopo Lenkowicz; Ida Paris; Ettore Domenico Capoluongo; Antonia Carla Testa; Anna Fagotti; Vincenzo Valentini; Giovanni Scambia
Journal:  Sci Rep       Date:  2020-10-05       Impact factor: 4.379

Review 9.  Radiomic biomarkers of tumor immune biology and immunotherapy response.

Authors:  Jarey H Wang; Kareem A Wahid; Lisanne V van Dijk; Keyvan Farahani; Reid F Thompson; Clifton David Fuller
Journal:  Clin Transl Radiat Oncol       Date:  2021-04-07

Review 10.  Radiogenomics of lung cancer.

Authors:  Chi Wah Wong; Ammar Chaudhry
Journal:  J Thorac Dis       Date:  2020-09       Impact factor: 3.005

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