Advertisement
Research Article| Volume 97, P13-24, May 2022

HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images

Published:March 22, 2022DOI:https://doi.org/10.1016/j.ejmp.2022.03.010

      Highlights

      • Phantoms are a useful tool to study feature repeatability and reproducibility.
      • The proposed phantom contains heterogeneous inserts to mimic lung tumour texture.
      • A commercial phantom with homogeneous inserts is not suitable to mimic tumours.
      • Feature repeatability is texture dependent.
      • Most features are repeatable, especially from firstorder and glcm categories.

      Abstract

      Purpose

      Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions.

      Methods

      We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10.

      Results

      The best similarity with the patients was obtained with the polyacrylate inserts (55.6–90.2%), the worst with Catphan (15.7–19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3–100% at 120kVp, 75.7–97.9% at 100kVp), and observed a texture dependency in repeatability.

      Conclusions

      Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Kumar V.
        • Gu Y.
        • Basu S.
        • Berglund A.
        • Eschrich S.A.
        • Schabath M.B.
        • et al.
        QIN “Radiomics: The Process and the Challenges”.
        Magn Reson Imag. 2012; 30: 1234-1248https://doi.org/10.1016/j.mri.2012.06.010
        • Aerts H.J.W.L.
        • Velazquez E.R.
        • Leijenaar R.T.H.
        • Parmar C.
        • Grossmann P.
        • Carvalho S.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat Commun. 2014; 5https://doi.org/10.1038/ncomms5006
        • Gillies R.J.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577https://doi.org/10.1148/radiol.2015151169
        • Papadimitroulas P.
        • Brocki L.
        • Christopher Chung N.
        • Marchadour W.
        • Vermet F.
        • Gaubert L.
        • et al.
        Artificial intelligence: deep learning in oncological radiomics and challenges of interpretability and data harmonization.
        Phys Med. 2021; 83: 108-121https://doi.org/10.1016/j.ejmp.2021.03.009
        • Avanzo M.
        • Stancanello J.
        • El Naqa I.
        Beyond imaging: The promise of radiomics.
        Phys Med. 2017; 38: 122-139https://doi.org/10.1016/j.ejmp.2017.05.071
        • Sollini M.
        • Antunovic L.
        • Chiti A.
        • Kirienko M.
        Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.
        Eur J Nucl Med Mol Imaging. 2019; 46: 2656-2672https://doi.org/10.1007/s00259-019-04372-x
        • Schick U.
        • Lucia F.
        • Bourbonne V.
        • Dissaux G.
        • Pradier O.
        • Jaouen V.
        • et al.
        Use of radiomics in the radiation oncology setting: where do we stand and what do we need?.
        Cancer/Radiother. 2020; 24: 755-761https://doi.org/10.1016/j.canrad.2020.07.005
        • Wang J.H.
        • Wahid K.A.
        • van Dijk L.V.
        • Farahani K.
        • Thompson R.F.
        • Fuller C.D.
        Radiomic biomarkers of tumor immune biology and immunotherapy response.
        Clin Transl Radiat Oncol. 2021; 28: 97-115https://doi.org/10.1016/j.ctro.2021.03.006
        • Caruso D.
        • Polici M.
        • Zerunian M.
        • Pucciarelli F.
        • Guido G.
        • Polidori T.
        • et al.
        Radiomics in oncology, Part 2: thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal applications.
        Cancers. 2021; 13: 2681https://doi.org/10.3390/cancers13112681
        • Pinto Dos Santos D.
        • Dietzel M.
        • Baessler B.
        A decade of radiomics research: are images really data or just patterns in the noise?.
        Eur Radiol. 2021; 31: 1-4https://doi.org/10.1007/s00330-020-07108-w
        • Traverso A.
        • Wee L.
        • Dekker A.
        • Gillies R.
        Repeatability and reproducibility of radiomic features: a systematic review.
        Int J Radiat Oncol Biol Phys. 2018; 102: 1143-1158https://doi.org/10.1016/j.ijrobp.2018.05.053
        • Park J.E.
        • Park S.Y.
        • Kim H.J.
        • Kim H.S.
        Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives.
        Korean J Radiol. 2019; 20: 1124-1137https://doi.org/10.3348/kjr.2018.0070
        • Fornacon-Wood I.
        • Faivre-Finn C.
        • O’Connor J.P.B.
        • Price G.J.
        Radiomics as a personalized medicine tool in lung cancer: separating the hope from the hype.
        Lung Cancer. 2020; 146: 197-208https://doi.org/10.1016/j.lungcan.2020.05.028
        • van Timmeren J.E.
        • Cester D.
        • Tanadini-Lang S.
        • Alkadhi H.
        • Baessler B.
        Radiomics in medical imaging—“how-to” guide and critical reflection.
        Insights Imaging. 2020; 11: 91https://doi.org/10.1186/s13244-020-00887-2
        • Zwanenburg A.
        • Vallières M.
        • Abdalah M.A.
        • Aerts H.J.W.L.
        • Andrearczyk V.
        • Apte A.
        • et al.
        The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.
        Radiology. 2020; 295: 328-338https://doi.org/10.1148/radiol.2020191145
        • Espinasse M.
        • Pitre-Champagnat S.
        • Charmettant B.
        • Bidault F.
        • Volk A.
        • Balleyguier C.
        • et al.
        CT texture analysis challenges: influence of acquisition and reconstruction parameters. A comprehensive review.
        Diagnostics. 2020; 10: 258https://doi.org/10.3390/diagnostics10050258
        • Larue R.T.H.M.
        • van Timmeren J.E.
        • de Jong E.E.C.
        • Feliciani G.
        • Leijenaar R.T.H.
        • Schreurs W.M.J.
        • et al.
        Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study.
        Acta Oncol. 2017; 56: 1544-1553https://doi.org/10.1080/0284186X.2017.1351624
        • Meyer M.
        • Ronald J.
        • Vernuccio F.
        • Nelson R.C.
        • Ramirez-Giraldo J.C.
        • Solomon J.
        • et al.
        Reproducibility of CT Radiomic features within the same patient: influence of radiation dose and CT reconstruction settings.
        Radiology. 2019; 293: 583-591https://doi.org/10.1148/radiol.2019190928
        • Park S.
        • Lee S.M.
        • Do K.-H.
        • Lee J.-G.
        • Bae W.
        • Park H.
        • et al.
        Deep learning algorithm for reducing CT slice thickness: effect on reproducibility of radiomic features in lung cancer.
        Korean J Radiol. 2019; 20: 1431-1440https://doi.org/10.3348/kjr.2019.0212
        • Erdal B.S.
        • Demirer M.
        • Little K.J.
        • Amadi C.C.
        • Ibrahim G.F.M.
        • O’Donnell T.P.
        • et al.
        Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?.
        PLoS ONE. 2020; 15: e0240184https://doi.org/10.1371/journal.pone.0240184
        • Varghese B.A.
        • Hwang D.
        • Cen S.Y.
        • Lei X.
        • Levy J.
        • Desai B.
        • et al.
        Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.
        J Appl Clin Med Phys. 2021; 22: 98-107https://doi.org/10.1002/acm2.13162
        • Prezzi D.
        • Owczarczyk K.
        • Bassett P.
        • Siddique M.
        • Breen D.J.
        • Cook G.J.R.
        • et al.
        Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer.
        Eur Radiol. 2019; 29: 5227-5235https://doi.org/10.1007/s00330-019-06073-3
        • Sung P.
        • Lee J.M.
        • Joo I.
        • Lee S.
        • Kim T.H.
        • Ganeshan B.
        Evaluation of the impact of iterative reconstruction algorithms on computed tomography texture features of the liver parenchyma using the filtration-histogram method.
        Korean J Radiol. 2019; 20: 558-568https://doi.org/10.3348/kjr.2018.0368
        • Mackin D.
        • Fave X.
        • Zhang L.
        • Fried D.
        • Yang J.
        • Taylor B.
        • et al.
        Measuring computed tomography scanner variability of radiomics features.
        Invest Radiol. 2015; 50: 757-765https://doi.org/10.1097/RLI.0000000000000180
        • Ger R.B.
        • Zhou S.
        • Chi P.-C.
        • Lee H.J.
        • Layman R.R.
        • Jones A.K.
        • et al.
        Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies.
        Sci Rep. 2018; 8https://doi.org/10.1038/s41598-018-31509-z
        • Kakino R.
        • Nakamura M.
        • Mitsuyoshi T.
        • Shintani T.
        • Hirashima H.
        • Matsuo Y.
        • et al.
        Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients.
        Phys Med. 2020; 69: 176-182https://doi.org/10.1016/j.ejmp.2019.12.019
        • Tamponi M.
        • Crivelli P.
        • Montella R.
        • Sanna F.
        • Gabriele D.
        • Poggiu A.
        • et al.
        Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging.
        Phys Med. 2021; 82: 321-331https://doi.org/10.1016/j.ejmp.2021.02.014
        • Parmar C.
        • Rios Velazquez E.
        • Leijenaar R.
        • Jermoumi M.
        • Carvalho S.
        • Mak R.H.
        • et al.
        Robust Radiomics feature quantification using semiautomatic volumetric segmentation.
        PLoS ONE. 2014; 9: e102107https://doi.org/10.1371/journal.pone.0102107
        • Pavic M.
        • Bogowicz M.
        • Würms X.
        • Glatz S.
        • Finazzi T.
        • Riesterer O.
        • et al.
        Influence of inter-observer delineation variability on radiomics stability in different tumor sites.
        Acta Oncol. 2018; 57: 1070-1074https://doi.org/10.1080/0284186X.2018.1445283
        • Haarburger C.
        • Müller-Franzes G.
        • Weninger L.
        • Kuhl C.
        • Truhn D.
        • Merhof D.
        Radiomics feature reproducibility under inter-rater variability in segmentations of CT images.
        Sci Rep. 2020; 10: 12688https://doi.org/10.1038/s41598-020-69534-6
        • Shafiq-ul-Hassan M.
        • Zhang G.G.
        • Latifi K.
        • Ullah G.
        • Hunt D.C.
        • Balagurunathan Y.
        • et al.
        Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels.
        Med Phys. 2017; 44: 1050-1062https://doi.org/10.1002/mp.12123
        • Wang H.Y.C.
        • Donovan E.M.
        • Nisbet A.
        • South C.P.
        • Alobaidli S.
        • Ezhil V.
        • et al.
        The stability of imaging biomarkers in radiomics: a framework for evaluation.
        Phys Med Biol. 2019; 64: 165012https://doi.org/10.1088/1361-6560/ab23a7
        • Ligero M.
        • Jordi-Ollero O.
        • Bernatowicz K.
        • Garcia-Ruiz A.
        • Delgado-Muñoz E.
        • Leiva D.
        • et al.
        Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis.
        Eur Radiol. 2021; 31: 1460-1470https://doi.org/10.1007/s00330-020-07174-0
        • Chatterjee A.
        • Valliéres M.
        • Forghani R.
        • Seuntjens J.
        Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data.
        Phys Med. 2021; 88: 272-277https://doi.org/10.1016/j.ejmp.2021.07.023
      1. International Vocabulary of Metrology – Basic and General Concepts and Associated Terms. 3rd ed. JCGM 200:2012.

        • Rinaldi L.
        • De Angelis S.P.
        • Raimondi S.
        • Rizzo S.
        • Fanciullo C.
        • Rampinelli C.
        • et al.
        Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.
        Eur Radiol Exp. 2022; 6https://doi.org/10.1186/s41747-021-00258-6
        • Berenguer R.
        • Pastor-Juan M.D.R.
        • Canales-Vázquez J.
        • Castro-García M.
        • Villas M.V.
        • Mansilla Legorburo F.
        • et al.
        Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters.
        Radiology. 2018; 288: 407-415https://doi.org/10.1148/radiol.2018172361
        • Mahmood U.
        • Apte A.
        • Kanan C.
        • Bates D.D.B.
        • Corrias G.
        • Manneli L.
        • et al.
        Quality control of radiomic features using 3D-printed CT phantoms.
        J Med Imaging. 2021; 8033505https://doi.org/10.1117/1.JMI.8.3.033505
        • Tino R.
        • Yeo A.
        • Leary M.
        • Brandt M.
        • Kron T.
        A systematic review on 3D-printed imaging and dosimetry phantoms in radiation therapy.
        Technol Cancer Res Treat. 2019; 181533033819870208https://doi.org/10.1177/1533033819870208
        • Valladares A.
        • Beyer T.
        • Rausch I.
        Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: an overview of existing designs.
        Med Phys. 2020; 47: 2023-2037https://doi.org/10.1002/mp.14045
        • Bianchini L.
        • Botta F.
        • Origgi D.
        • Rizzo S.
        • Mariani M.
        • Summers P.
        • et al.
        PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis.
        Phys Med. 2020; 71: 71-81https://doi.org/10.1016/j.ejmp.2020.02.003
        • Sindi R.
        • Wong Y.H.
        • Yeong C.H.
        • Sun Z.
        Development of patient-specific 3D-printed breast phantom using silicone and peanut oils for magnetic resonance imaging.
        Quant Imaging Med Surg. 2020; 10: 1237-1248https://doi.org/10.21037/qims-20-251
        • Forgacs A.
        • Pall Jonsson H.
        • Dahlbom M.
        • Daver F.
        • DiFranco D.M.
        • Opposits G.
        • et al.
        A study on the basic criteria for selecting heterogeneity parameters of F18-FDG PET images.
        PloS One. 2016; 11e0164113https://doi.org/10.1371/journal.pone.0164113
        • Presotto L.
        • Bettinardi V.
        • De Bernardi E.
        • Belli M.L.
        • Cattaneo G.M.
        • Broggi S.
        • et al.
        PET textural features stability and pattern discrimination power for radiomics analysis: An “ad-hoc” phantoms study.
        Phys Med. 2018; 50: 66-74https://doi.org/10.1016/j.ejmp.2018.05.024
        • Gallivanone F.
        • Interlenghi M.
        • D’Ambrosio D.
        • Trifirò G.
        • Castiglioni I.
        Parameters influencing PET Imaging Features: A Phantom Study With Irregular And Heterogeneous Synthetic Lesions.
        Contrast Media Mol Imaging. 2018; 2018: 5324517https://doi.org/10.1155/2018/5324517
        • Carles M.
        • Fechter T.
        • Martí-Bonmatí L.
        • Baltas D.
        • Mix M.
        Experimental phantom evaluation to identify robust positron emission tomography (PET) radiomic features.
        EJNMMI Phys. 2021; 8: 46https://doi.org/10.1186/s40658-021-00390-7
        • Samei E.
        • Hoye J.
        • Zheng Y.
        • Solomon J.B.
        • Marin D.
        Design and fabrication of heterogeneous lung nodule phantoms for assessing the accuracy and variability of measured texture radiomics features in CT.
        J Med Imaging. 2019; 6: 021606https://doi.org/10.1117/1.JMI.6.2.021606
        • Varghese B.A.
        • Hwang D.
        • Cen S.Y.
        • Levy J.
        • Liu D.
        • Lau C.
        • et al.
        Reliability of CT-based texture features: Phantom study.
        J Appl Clin Med Phys. 2019; 20: 155-163https://doi.org/10.1002/acm2.12666
        • Plautz T.E.
        • Zheng C.
        • Noid G.
        • Li X.A.
        Time stability of delta-radiomics features and the impact on patient analysis in longitudinal CT images.
        Med Phys. 2019; 46: 1663-1676https://doi.org/10.1002/mp.13395
        • Hong D.
        • Lee S.
        • Kim G.B.
        • Lee S.M.
        • Kim N.
        • Seo J.B.
        Development of a CT imaging phantom of anthromorphic lung using fused deposition modeling 3D printing.
        Medicine (Baltimore). 2020; 99e18617https://doi.org/10.1097/MD.0000000000018617
        • Mackin D
        • Fave X
        • Zhang L
        • Yang J
        • Jones AK
        • Ng CS
        • et al.
        Harmonizing the pixel size in retrospective computed tomography radiomics studies.
        PloS One. 2017; 12e0178524https://doi.org/10.1371/journal.pone.0178524
        • Mackin D.
        • Ger R.
        • Dodge C.
        • Fave X.
        • Chi P.-C.
        • Zhang L.
        • et al.
        Effect of tube current on computed tomography radiomic features.
        Sci Rep. 2018; 8: 2354https://doi.org/10.1038/s41598-018-20713-6
        • Levine Z.H.
        • Chen-Mayer H.H.
        • Peskin A.P.
        • Pintar A.L.
        Comparison of one-dimensional and volumetric computed tomography measurements of injected-water phantoms.
        J Res Natl Inst Stand Technol. 2017; 122: 1-9https://doi.org/10.6028/jres.122.036
        • Fuse H.
        • Fujisaki T.
        • Ikeda R.
        • Hakani Z.
        Applicability of lung equivalent phantom using the cork with absorbed water in radiotherapeutic dosimetry.
        Int J Med Phys Clin Eng Radiat Oncol. 2018; 7: 27-34https://doi.org/10.4236/ijmpcero.2018.71003
        • Botta F.
        • Raimondi S.
        • Rinaldi L.
        • Bellerba F.
        • Corso F.
        • Bagnardi V.
        • et al.
        Association of a CT-based clinical and radiomics score of non-small cell lung cancer (NSCLC) with lymph node status and overall survival.
        Cancers. 2020; 12: 1432https://doi.org/10.3390/cancers12061432
        • van Griethuysen J.J.M.
        • Fedorov A.
        • Parmar C.
        • Hosny A.
        • Aucoin N.
        • Narayan V.
        • et al.
        Computational radiomics system to decode the radiographic phenotype.
        Cancer Res. 2017; 77: e104-e107https://doi.org/10.1158/0008-5472.CAN-17-0339
        • Traverso A.
        • Kazmierski M.
        • Zhovannik I.
        • Welch M.
        • Wee L.
        • Jaffray D.
        • et al.
        Machine learning helps identifying volume-confounding effects in radiomics.
        Phys Med. 2020; 71: 24-30https://doi.org/10.1016/j.ejmp.2020.02.010
        • Shafiq-ul-Hassan M.
        • Zhang G.G.
        • Hunt D.C.
        • Latifi K.
        • Ullah G.
        • Gillies R.J.
        • et al.
        Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra.
        J Med Imaging. 2018; 5: 011013https://doi.org/10.1117/1.JMI.5.1.011013
        • McNitt-Gray M.
        • Napel S.
        • Jaggi A.
        • Mattonen S.A.
        • Hadjiiski L.
        • Muzi M.
        • et al.
        Standardization in quantitative imaging: a multicenter comparison of radiomic features from different software packages on digital reference objects and patient data sets.
        Tomography. 2020; 6: 118-128https://doi.org/10.18383/j.tom.2019.00031
        • Lu L.
        • Sun S.H.
        • Afran A.
        • Yang H.
        • Lu Z.F.
        • So J.
        • et al.
        Identifying robust radiomics features for lung cancer by using in-vivo and phantom lung lesions.
        Tomography. 2021; 7: 55-64https://doi.org/10.3390/tomography7010005
        • Gallivanone F.
        • Interlenghi M.
        • D’Ambrosio D.
        • Fantinato D.
        • Alberizzi L.
        • Trifirò G.
        • et al.
        An anthropomorphic phantom for advanced image processing of realistic 18F-FDG PET-CT oncological studies. 2016.
        IEEE Nucl Sci Symp Med Imaging Conf Room-Temp Semicond Detect Workshop (NSS/MIC/RTSD). 2016; : 1-7https://doi.org/10.1109/NSSMIC.2016.8069418
        • Jha A.K.
        • Mithun S.
        • Jaiswar V.
        • Sherkhane U.B.
        • Purandare N.C.
        • Prabhash K.
        • et al.
        Repeatability and reproducibility study of radiomic features on a phantom and human cohort.
        Sci Rep. 2021; 11: 2055https://doi.org/10.1038/s41598-021-81526-8
        • Koo T.K.
        • Li M.Y.
        A guideline of selecting and reporting intraclass correlation coefficients for reliability research.
        J Chiropr Med. 2016; 15: 155-163https://doi.org/10.1016/j.jcm.2016.02.012
        • Pallotta S.
        • Cusumano D.
        • Taddeucci A.
        • Benelli M.
        • Sulejmeni R.
        • Lenkowicz J.
        • et al.
        PO-1536: RadiomiK: a phantom to test repeatability and reproducibility of CT-derived Radiomic Features.
        Radiotherapy and Oncology. 2020; 152: S830-S831https://doi.org/10.1016/S0167-8140(21)01554-1
        • Muenzfeld H.
        • Nowak C.
        • Riedlberger S.
        • Hartenstein A.
        • Hamm B.
        • Jahnke P.
        • et al.
        Intra-scanner repeatability of quantitative imaging features in a 3D printed semi-anthropomorphic CT phantom.
        Eur J Radiol. 2021; 141: 109818https://doi.org/10.1016/j.ejrad.2021.109818
        • Lennie E.
        • Tsoumpas C.
        • Sourbron S.
        Multimodal phantoms for clinical PET/MRI.
        EJNMMI Phys. 2021; 8: 62https://doi.org/10.1186/s40658-021-00408-0
        • Bianchini L.
        Novel phantoms for robust MRI-based radiomics in oncology.
        Università degli studi di Milano, Dipartimento di Fisica Aldo Pontremoli, 2020 (http://hdl.handle.net/2434/772014)