Letteratura scientifica selezionata sul tema "Coefficient de Diffusion Apparent (ADC)"
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Articoli di riviste sul tema "Coefficient de Diffusion Apparent (ADC)":
Chen, Haisong, Zengjie Wu, Wenjian Xu, Jing Pang, Meng Jia, Cheng Dong e Xiaoli Li. "Evaluating the Scope of Malignant Bone Tumor Using ADC Measurement on ADC Map". Technology in Cancer Research & Treatment 18 (1 gennaio 2019): 153303381985326. http://dx.doi.org/10.1177/1533033819853267.
Teixeira, Sara Reis, Paula Condé Lamparelli Elias, Andrea Farias de Melo Leite, Tatiane Mendes Gonçalves de Oliveira, Valdair Francisco Muglia e Jorge Elias Junior. "Apparent diffusion coefficient of normal adrenal glands". Radiologia Brasileira 49, n. 6 (dicembre 2016): 363–68. http://dx.doi.org/10.1590/0100-3984.2015.0045.
Verbanck, Sylvia, e Manuel Paiva. "Acinar determinants of the apparent diffusion coefficient for helium-3". Journal of Applied Physiology 108, n. 4 (aprile 2010): 793–99. http://dx.doi.org/10.1152/japplphysiol.01230.2009.
Sumikawa, Tetsuo, Hidetake Yabuuchi, Chiharu Sumikawa, Yoshiteru Nakashima e Gouji Miura. "Influence of blade width and magnetic field strength on the ADC on PROPELLER DWI in head and neck". Neuroradiology Journal 33, n. 1 (13 agosto 2019): 39–47. http://dx.doi.org/10.1177/1971400919870178.
Ignjatovic, Jelena, Dragan Stojanov, Vladimir Zivkovic, Srdjan Ljubisavljevic, Nebojsa Stojanovic, Ivan Stefanovic, Daniela Benedeto-Stojanov et al. "Apparent diffusion coefficient in the evaluation of cerebral gliomas malignancy". Vojnosanitetski pregled 72, n. 10 (2015): 870–75. http://dx.doi.org/10.2298/vsp140229073i.
Usuda, Katsuo, Shun Iwai, Aika Yamagata, Yoshihito Iijima, Nozomu Motono, Mariko Doai, Munetaka Matoba, Keiya Hirata e Hidetaka Uramoto. "How to Discriminate Lung Cancer From Benign Pulmonary Nodules and Masses? Usefulness of Diffusion-Weighted Magnetic Resonance Imaging With Apparent Diffusion Coefficient and Inside/Wall Apparent Diffusion Coefficient Ratio". Clinical Medicine Insights: Oncology 15 (gennaio 2021): 117955492110148. http://dx.doi.org/10.1177/11795549211014863.
Wang, Rui, Weidong Liu, Fang Ren e Jing Ren. "Comparative study of diagnostic value between IVIM and DWI for prostate cancer at 3.0 T magnetic resonance". Chinese Journal of Academic Radiology 4, n. 3 (19 agosto 2021): 186–93. http://dx.doi.org/10.1007/s42058-021-00079-x.
Kanamoto, Hirohito, Masaki Norimoto, Yawara Eguchi, Yasuhiro Oikawa, Sumihisa Orita, Kazuhide Inage, Koki Abe et al. "Evaluating Spinal Canal Lesions Using Apparent Diffusion Coefficient Maps with Diffusion-Weighted Imaging". Asian Spine Journal 14, n. 3 (30 giugno 2020): 312–19. http://dx.doi.org/10.31616/asj.2019.0266.
Basirjafari, Sedigheh, Masoud Poureisa, Babak Shahhoseini, Mohammad Zarei, Saeideh Aghayari Sheikh Neshin, Sheida Anvari Aria e Masoud Nouri-Vaskeh. "Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI". Acta Radiologica 61, n. 2 (2 luglio 2019): 244–52. http://dx.doi.org/10.1177/0284185119856887.
Sahoo, Prativa, Russell C. Rockne, Alexander Jung, Pradeep K. Gupta, Ram K. S. Rathore e Rakesh K. Gupta. "Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in Prostate". Prostate Cancer 2020 (10 febbraio 2020): 1–7. http://dx.doi.org/10.1155/2020/5091218.
Tesi sul tema "Coefficient de Diffusion Apparent (ADC)":
Onishi, Natsuko. "Apparent Diffusion Coefficient as a Potential Surrogate Marker for Ki-67 Index in Mucinous Breast Carcinoma". 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225449.
Kuwahara, Ryo. "A Predictor of Tumor Recurrence in Patients With Endometrial Carcinoma After Complete Resection of the Tumor: The Role of Pretreatment Apparent Diffusion Coefficient". Kyoto University, 2020. http://hdl.handle.net/2433/253483.
Flötotto, Felix [Verfasser], e Christian [Akademischer Betreuer] Habermann. "Bestimmung des Einflusses von Alter und des Zigarettenkonsums auf die erhobenen ADC-Werte (apparent-diffusion-coefficient) der Glandula submandibularis / Felix Flötotto. Betreuer: Christian Habermann". Hamburg : Staats- und Universitätsbibliothek Hamburg, 2013. http://d-nb.info/1045024112/34.
Rabaszowski, Svenja [Verfasser], Gerald [Gutachter] Antoch e Hans-Jürgen [Gutachter] Laws. "Diffusionswichtung und der Apparent Diffusion Coefficient (ADC) zur Diagnostik und Differenzierung von Bauchtumoren bei pädiatrischen Patienten / Svenja Rabaszowski ; Gutachter: Gerald Antoch, Hans-Jürgen Laws". Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2021. http://d-nb.info/1239893736/34.
Jaouen, Tristan. "Caractérisation du cancer de la prostate de haut grade à l’IRM multiparamétrique à l’aide d’un système de diagnostic assisté par ordinateur basé sur la radiomique et utilisé comme lecteur autonome ou comme second lecteur". Electronic Thesis or Diss., Lyon, 2022. http://www.theses.fr/2022LYSE1140.
We developed a region of interest-based (ROIs) computer-aided diagnosis system (CAD) to characterize International Society of Urological Pathology grade (ISUP) ≥2 prostate cancers at multiparametric MRI (mp-MRI). Image parameters from two multi-vendor datasets of 265 pre-prostatectomy and 112 pre-biopsy MRIs were combined using logistic regression. The best models used the ADC 2nd percentile (ADC2) and normalized wash-in rate (WI) in the peripheral zone (PZ) and the ADC 25th percentile (ADC25) in the transition zone (TZ). They were combined in the CAD system. The CAD was retrospectively assessed on two multi-vendor datasets containing respectively 158 and 105 pre-biopsy MRIs from our institution (internal test dataset) and another institution (external test dataset). Two radiologists independently outlined lesions targeted at biopsy. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score prospectively assigned at biopsy and the CAD score were compared to biopsy findings. At patient level, the areas under the Receiver Operating Characteristic curve (AUC) of the PI-RADSv2 score were 82% (95% CI: 74-87) and 85% (95% CI: 79-91) in the internal and external test datasets respectively. For both radiologists, the CAD score had similar AUC results in the internal (82%, 95% CI: 76-89, p=1; 84%, 95% CI: 78-91, p=1) and external (82%, 95% CI: 76-89, p=0.82; 86%, 95% CI: 79-93, p=1) test datasets. Combining PI-RADSv2 and CAD findings could have avoided 41-52% of biopsies while missing 6-10% of ISUP≥2 cancers. The CAD system confirmed its robustness showing good discrimination of ISUP ≥2 cancers in a multicentric study involving 22 different scanners with highly heterogeneous image protocols. In per patient analysis, the CAD and the PI-RADSv2 had similar AUC values (76%, 95% CI: 70-82 vs 79%, 95% CI: 73-86; p=0.34) and sensitivities (86%, 95% CI: 76-96 vs 89%, 95% CI: 79-98 for PI-RADSv2 ≥4). The specificity of the CAD (62%, 95% CI: 53-70 vs 49%, 95% CI: 39-59 for PI-RADSv2 ≥4) could be used to complement the PI-RADSv2 score and potentially avoid 50% of biopsies, while missing 13% of ISUP ≥2 cancers. These findings were very similar to those reported in the single center test cohorts. Given its robustness, the CAD could then be exploited in more specific applications. The CAD first provided good discrimination of ISUP ≥2 cancers in patients under Active Surveillance. Its AUC (80%, 95% CI: 74-86) was similar to that of the PI-RADS score prospectively assigned by specialized uro-radiologists at the time of biopsy (81%, 95% CI: 74-87; p=0.96). After dichotomization, the CAD was more specific than the PI-RADS ≥3 (p<0.001) and the PI-RADS ≥4 scores (p<0.001). It could offer a solution to select patients who could safely avoid confirmatory or follow-up biopsy during Active Surveillance (25%), while missing 5% of ISUP≥2 cancers. Finally, the CAD was tested with the pre-prostatectomy mp-MRIs of 56 Japanese patients, from a population which is geographically distant from its training population and which is of interest because of its low prostate cancer incidence and mortality. The CAD obtained an AUC similar to the PI-RADSv2 score assigned by an experience radiologist in the PZ (80%, 95% CI: 71-90 vs 80%, 95% CI: 71-89; p=0.886) and in the TZ (79%, 95% CI: 66-90 vs 93%, 95%CI: 82-96; p=0.051). These promising and robust results across heterogeneous datasets suggest that the CAD could be used in clinical routine as a second opinion reader to help select the patients who could safely avoid biopsy. This CAD may assist less experience readers in the characterization of prostate lesions
Moraes, M?rcia Cristina Gon?alves de Oliveira. "O estudo da acur?cia da resson?ncia magn?tica multiparam?trica no diagn?stico do c?ncer de pr?stata". Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2017. http://tede2.pucrs.br/tede2/handle/tede/8227.
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Abstract: Today, the incidence of prostate cancer is considered high, however, unlike other malignant tumours, there is an expressive number of cases in which prostate cancer does not progress to clinical disease. The management of patients with prostate cancer should be individually fitted due to the broad behaviour spectrum of this cancer, ranging from low grade tumours with low aggressive biological characteristics to high grade tumours with metastatic capacity. The possibility of predicting the future behavior of the disease allows the selection of the most appropriate conduct for each case. Studies have shown that mpMRI (multiparametric Magnetic Resonance Imaging) has a high negative predictive value for clinically significant prostate cancer, indicating that its application as a screening method and as assessment method of disease progression is promising. To standardize the protocols and reports of prostate mpMRI, the PI-RADS v2 (Prostate Imaging Reporting and Data System version 2) was launched in 2015. Multiparametric Magnetic Resonance Imaging standardized by PI-RADSv2 has been taking a prominent place in the management of prostate cancer, but the specificity and positive predictive value still need to be improved. Purpose: To assess whether the ADC (Apparent diffusion coefficient) value and tumour ADC ratio associated with PI-RADS v2 may increase accuracy in predicting clinically significant prostate cancer. Materials and methods: 91 individuals with suspected prostate cancer were retrospectively studied through mpMRI imaging standardized by PI-RADS v2, obtaining the ADC value from the tumour and the contralateral tissue. The findings were correlated to anatomopathological study (biopsy, prostatectomy or transurethral resection). Results: Accuracy, sensitivity, specificity, positive predictive value and negative predictive value for the consensus between the two reviewers using PI-RADS v2, category 3 associated with categories 4 and 5 for the detection of clinically significant cancer were 70.3%, 97.4%, 50.9%, 58.7% and 96.4% (p <0.001), respectively. The association of the ADC value (<0.795x10-3) to categories 3, 4 and 5 of the PI-RADSv2, in turn, demonstrated accuracy, specificity and positive predictive value of 78.9%, 84.9% and 76.5%; and the association with the tumour ADC ratio (<0.62) presented 77.5%, 86.5% and 77.4% of accuracy, specificity and positive predictive value, respectively. Conclusion: The association of the ADC value and the tumour ADC ratio to the PI-RADS v2 in mpMRI increases the accuracy, specificity and positive predictive value in the detection of aggressive prostate cancer, and may help in the screening of individuals who would undergo invasive procedures and radical therapy, or conservative management, as active surveillance or watchful waiting.
Introdu??o: ? considerada alta a incid?ncia de c?ncer de pr?stata na atualidade, contudo, diferentemente de outras neoplasias, existe um n?mero expressivo de casos em que o c?ncer de pr?stata n?o evolui para a doen?a cl?nica. Por este motivo, o manejo dos pacientes com neoplasia prost?tica deve ser moldado individualmente face ao amplo espectro que varia desde tumores de baixo grau, com caracter?sticas biol?gicas de baixa agressividade, a tumores de alto grau, com capacidade metast?tica. A possibilidade de prever o comportamento futuro da doen?a permite a sele??o da conduta mais adequada para cada caso. Estudos vem comprovando que a Resson?ncia Magn?tica multiparam?trica (RMmp) apresenta um alto valor preditivo negativo para o c?ncer de pr?stata com signific?ncia cl?nica, indicando que sua aplica??o como m?todo de triagem e na avalia??o da progress?o da doen?a ? promissora. Para padronizar os protocolos e os relat?rios da RMmp da pr?stata foi lan?ado em 2015 o PI-RADS v2 (?Prostate Imaging Reporting and Data System? vers?o 2). A RMmp padronizada pelo PI-RADS v2 vem assumindo um lugar de destaque no manejo do c?ncer de pr?stata, contudo, ainda s?o considerados baixos a Especificidade e o Valor Preditivo Positivo. Objetivos: Avaliar se o valor de ADC (?Apparent diffusion coefficient? = Coeficiente de Difus?o Aparente) e a raz?o tumoral do ADC associados ao PI-RADS v2 podem aumentar a acur?cia da RMmp na predi??o do c?ncer de pr?stata com signific?ncia clinica. Materiais e m?todos: Foram estudados retrospectivamente 91 indiv?duos com suspeita de c?ncer de pr?stata, submetidos a RMmp padronizada pelo PI-RADS v2, obtendo-se o ADC quantitativo da les?o e do tecido contralateral. Os achados foram correlacionados ao estudo anatomopatol?gico (bi?psia, prostatectomia ou ressec??o transuretral). Resultados: A acur?cia, sensibilidade, especificidade, valor preditivo positivo e valor preditivo negativo para o consenso entre os dois avaliadores utilizando a RMmp padronizada pelo PI-RADS v2, com a categoria 3 associada as categorias 4 e 5 para a detec??o do c?ncer com signific?ncia cl?nica foram 70,3%; 97,4%; 50,9%; 58,7% e 96,4% (p<0,001), respectivamente. A associa??o do valor do ADC (<0,795x10-3) ?s categorias 3, 4 e 5 do PI-RADS v2, por sua vez, demonstrou acur?cia, especificidade e valor preditivo positivo de 78,9%; 84,9% e 76,5%; e a associa??o com a raz?o tumoral do ADC (< 0,62), apresentou 77,5%; 86,5% e 77,4% de acur?cia, especificidade e valor preditivo positivo, respectivamente. Conclus?o: A associa??o do valor do ADC e da raz?o tumoral do ADC ao PI-RADS v2 na RMmp aumenta a acur?cia, especificidade e valor preditivo positivo na detec??o do c?ncer agressivo da pr?stata, podendo auxiliar na triagem dos indiv?duos e na decis?o entre a conduta agressiva, com procedimentos invasivos e terapia radical, ou a conduta conservadora, com vigil?ncia ativa ou observa??o.
Demir, Ayhan. "Apport de l'imagerie de diffusion avec calcul du coefficient apparent de diffusion et du tenseur de diffusion dans la myélopathie cervicarthrosique". Bordeaux 2, 2000. http://www.theses.fr/2000BOR23058.
Silva, Matthew S. "NMR characterization of changes in the apparent diffusion coefficient of water following transient cerebral ischemia". Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0327102-221251.
Iima, Mami. "Apparent Diffusion Coefficient as an MR Imaging Biomarker of Low-Risk Ductal Carcinoma in Situ: A Pilot Study". Kyoto University, 2014. http://hdl.handle.net/2433/188640.
Gauthier, Yvan. "Measurement of the apparent diffusion coefficient of water in white matter using magnetic resonance imaging, a phantom study". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0016/MQ48500.pdf.
Capitoli di libri sul tema "Coefficient de Diffusion Apparent (ADC)":
Fiebach, J. B., P. D. Schellinger, S. Heiland e K. Sartor. "Conventional MRI, Diffusion-weighted MRI (DWI) and Apparent Diffusion Coefficient (ADC)". In Stroke MRI, 13–21. Heidelberg: Steinkopff, 2003. http://dx.doi.org/10.1007/978-3-642-57387-3_4.
Jerome, Neil Peter, e João S. Periquito. "Analysis of Renal Diffusion-Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intravoxel Incoherent Motion (IVIM) Models". In Methods in Molecular Biology, 611–35. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_37.
Osawa, T., M. Mase, T. Miyati, H. Kan, K. Demura, H. Kasai, M. Hara, Y. Shibamoto e K. Yamada. "Delta-ADC (Apparent Diffusion Coefficient) Analysis in Patients with Idiopathic Normal Pressure Hydrocephalus". In Acta Neurochirurgica Supplementum, 197–200. Vienna: Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-0956-4_38.
Ikezaki, Kiyonobu, M. Takahashi, H. Koga, J. Kawai, Z. Kovács, T. Inamura e M. Fukui. "Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Contrast (MTC) Mapping of Experimental Brain Tumor". In Brain Edema X, 170–72. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-6837-0_52.
Jerome, Neil Peter, Anna Caroli e Alexandra Ljimani. "Renal Diffusion-Weighted Imaging (DWI) for Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), and Diffusion Tensor Imaging (DTI): Basic Concepts". In Methods in Molecular Biology, 187–204. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_11.
Chen, Y., W. Guo, Q. Zeng, X. Yan, M. Rao e Y. Liu. "Apparent Diffusion Coefficient Approximation and Diffusion Anisotropy Characterization in DWI". In Lecture Notes in Computer Science, 246–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11505730_21.
Yamasaki, Fumiyuki, Kazuhiko Sugiyama e Kaoru Kurisu. "Brain Tumors: Apparent Diffusion Coefficient at Magnetic Resonance Imaging". In Methods of Cancer Diagnosis, Therapy, and Prognosis, 279–96. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8665-5_22.
Kim, Yunho, Paul M. Thompson, Arthur W. Toga, Luminita Vese e Liang Zhan. "HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC". In Lecture Notes in Computer Science, 515–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02498-6_43.
Righini, A., C. Pierpaoli, J. R. Alger, M. Leonardi e G. Di Chiro. "Apparent diffusion coefficient alterations associated with experimental complex partial status epilepticus". In Proceedings of the XV Symposium Neuroradiologicum, 259–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79434-6_124.
Nakamura, Takashi, Misa Sumi e Marc Van Cauteren. "Salivary Gland Tumors: Preoperative Tissue Characterization with Apparent Diffusion Coefficient Mapping". In Methods of Cancer Diagnosis, Therapy, and Prognosis, 255–69. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3186-0_18.
Atti di convegni sul tema "Coefficient de Diffusion Apparent (ADC)":
Tulipano, P. Karina, William S. Millar, Celina Imielinska, Xin Liu, Joel Rosiene e Anthony L. D'Ambrosio. "Quantification of diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC) in the detection of acute stroke". In Medical Imaging, a cura di Armando Manduca e Amir A. Amini. SPIE, 2006. http://dx.doi.org/10.1117/12.653191.
Hussein, Khaled, Marwan Nasr Eldin, Hisham Mostafa e Ahmed Hamed. "Role of MRI Apparent Diffusion Coefficient (ADC) Quantification in the differentiation between benign and malignant pulmonary lesions". In ERS International Congress 2017 abstracts. European Respiratory Society, 2017. http://dx.doi.org/10.1183/1393003.congress-2017.pa3741.
Wati, Retno, Kouichi Ujita, Ayako Taketomi Takahashi e Yoshito Tsushima. "Effect of slice position on apparent diffusion coefficient (ADC) value of diffusion weighted whole-body imaging with background body signal suppression (DWIBS): A phantom study". In ADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0140503.
Dietzel, M., S. Ellmann, M. Hammon, M. Saake, R. Janker, M. Uder e P. Baltzer. "Einsatz des Apparent Diffusion Coefficient (ADC) zur Differentialdiagnose von gutartigen und bösartigen fokalen Leberläsionen (FLL): Auswirkungen des Postprocessing auf die diagnostische Genauigkeit?" In 101. Deutscher Röntgenkongress und 9. Gemeinsamer Kongress der DRG und ÖRG. © Georg Thieme Verlag KG, 2020. http://dx.doi.org/10.1055/s-0040-1703143.
Dietzel, M., B. Krug, P. Clauser, R. Schulz-Wendtland, M. Hellmich, H. Bickel, E. Wenkel et al. "Clinical management of patients with suspected breast-cancer: A multicentric comparison of Apparent Diffusion Coefficient Mapping (ADC) and the Kaiser Score (KS)". In Wissenschaftliche Abstracts zur 40. Jahrestagung der Deutschen Gesellschaft für Senologie e.V. (DGS) Interdisziplinär. Kommunikativ. Digital. Georg Thieme Verlag KG, 2021. http://dx.doi.org/10.1055/s-0041-1730151.
Dwihapsari, Yanurita, Dita Puspita Sari e Darminto. "The assessment of consistency using penetrometer and apparent diffusion coefficient (ADC) value using diffusion weighted magnetic resonance imaging (DW-MRI) from polyvinyl alcohol (PVA) formed by freezing-thawing cycle". In INTERNATIONAL CONFERENCE ON PHYSICS AND ITS APPLICATIONS: (ICPAP 2011). AIP, 2012. http://dx.doi.org/10.1063/1.4730686.
Martin, O., P. Heusch, J. Kirchner, N. Bruckmann, F. Nensa, L. Umutlu, G. Antoch e L. Sawicki. "Gibt es einen Zusammenhang zwischen immunhistochemischen Markern und Grading bei Bronchial-Ca mit dem apparent diffusion coefficient (ADC) und standardized uptake values (SUV) im PET/MRT?" In 100. Deutscher Röntgenkongress. Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0037-1682193.
Diniz, Camila Leal, Rosemar Macedo Sousa Rahal, Ruffo de Freitas-Júnior, Ilse Franco de Oliveira, Cristina Pinto Naldi Ruiz, Paulinelly Messias de Almeida, Marcelo Vilela Lauar e Lizzi Naldi Ruiz. "Magnetic resonance study of the breast: Diffusion sequence analysis". In Brazilian Breast Cancer Symposium 2023. Mastology, 2023. http://dx.doi.org/10.29289/259453942023v33s1071.
Adrada, Beatriz E., Abeer H. Abdelhafez, Benjamin C. Musall, Kenneth R. Hess, Jong Bum Son, Mark D. Pagel, Ken-Pin Hwang et al. "Abstract P6-02-03: Quantitative apparent diffusion coefficient (ADC) radiomics of tumor and peritumoral regions as potential predictors of treatment response to neoadjuvant chemotherapy (NACT) in triple negative breast cancer (TNBC) patients". In Abstracts: 2019 San Antonio Breast Cancer Symposium; December 10-14, 2019; San Antonio, Texas. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.sabcs19-p6-02-03.
Das, Saikat. "Low dose radiation and chemotherapy significantly reduces hypoxic cell population in locally advanced cervix cancer-results of a phase II study". In 16th Annual International Conference RGCON. Thieme Medical and Scientific Publishers Private Ltd., 2016. http://dx.doi.org/10.1055/s-0039-1685259.
Rapporti di organizzazioni sul tema "Coefficient de Diffusion Apparent (ADC)":
MR (Diffusion-Weighted Imaging (DWI) of the Apparent Diffusion Coefficient (ADC), Clinically Feasible Profile. Chair Michael Boss, Dariya Malyarenko e Daniel Margolis. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), dicembre 2022. http://dx.doi.org/10.1148/qiba/20221215.