BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:Categorization and characterization of uranium-bearing materials f or nuclear forensic attribution using ICP-MS DTSTART;VALUE=DATE-TIME:20260518T124100Z DTEND;VALUE=DATE-TIME:20260518T124900Z DTSTAMP;VALUE=DATE-TIME:20260624T101233Z UID:indico-contribution-643-3817@indico.tlabs.ac.za DESCRIPTION:Speakers: Khumoetsile Jonas (North-west university )\nThe incr easing risk of illicit trafficking and misuse of nuclear and radioactive m aterials has highlighted the importance of nuclear forensics in supporting radiological crime scene investigations and nuclear security. This study focuses on the categorization and characterization of uranium-bearing mate rials to support nuclear forensic attribution and investigative processes. Uranium materials originating from different stages of the nuclear fuel c ycle were analyzed using advanced analytical techniques to determine their physical\, chemical and isotopic signatures. Samples including Uranium or e and triuranium octoxide were prepared through crushing\,pulverization an d microwave digestion prior to analysis. Elemental and isotopic measuremen ts were performed using Inductively Coupled Plasma Mass Spectrometry. The se techniques enabled the determination of trace elements concentrations\, rare-earth elements (REE) patterns\, uranium isotopic ratios and lead iso topic ratios that serve as distinctive nuclear forensic signatures. \n\nTh e results demonstrate that uranium-bearing materials possess measurable el emental and isotopic characteristics that can be used to distinguish mater ials originating from different geological sources and processing stages. Rare-earth element distributions\, uranium and lead isotopic compositions provided valuable geochemical fingerprints that support source attribution . Overall\, the study highlights the importance of combining elemental and isotopic analysis for reliable categorization and characterization of ura nium-bearing materials. These signatures provide critical information for nuclear forensic investigations and may contribute to the development of a national nuclear forensic library to assist law enforcement and regulator y authorities in identifying the origin and history of intercepted nuclear materials.\n\nhttps://indico.tlabs.ac.za/event/139/contributions/3817/ LOCATION:NRF-iThemba LABS\, Old Faure Road\, Cape Town Auditorium URL:https://indico.tlabs.ac.za/event/139/contributions/3817/ END:VEVENT BEGIN:VEVENT SUMMARY:Comparison of tangential-intensity modulated radiotherapy (t-IMRT) and volumetric modulated arc therapy (VMAT) for different sizes of left b reast cancer DTSTART;VALUE=DATE-TIME:20260518T123300Z DTEND;VALUE=DATE-TIME:20260518T124100Z DTSTAMP;VALUE=DATE-TIME:20260624T101233Z UID:indico-contribution-643-3815@indico.tlabs.ac.za DESCRIPTION:Speakers: Abuobaida Ahmed (North-West University)\nBackground: Radiotherapy is an essential part of the management of left-sided breast cancer\, and this requires an optimal balance in target volume coverage an d organs at risk\, such as the ipsilateral lung and the heart. Advanced te chniques such as tangential-intensity modulated radiotherapy (t-IMRT) and volumetric modulated arc therapy (VMAT) are commonly used. However\, the d osimetric performance of t-IMRT and VMAT for varying breast sizes has been poorly characterized. \nAim: To compare the effectiveness of tangential-I MRT and VMAT for hypo-fractionated left-sided breast cancers across small\ , medium\, and large breast sizes.\nMaterials & Methods: A total of 30 CT datasets from female patients with left-sided breast cancer\, acquired bet ween 2020 and 2025\, were analyzed. Patients were divided into groups base d on breast volume\, with a mean volume of 781.00 cc. For each patient\, t wo treatment plans were designed using the Monaco treatment planning syste m\, which uses the Monte Carlo method. A dose of 26 Gy in 5 fractions was prescribed according to the FAST-Forward hypo-fractionated protocol. Evalu ation of the dosimetric parameters included the Planning Target Volume (PT V) coverage indices D2%\, D5%\, D95%\, Homogeneity Index (HI)\, Conformity Index (CI)\, and doses to the heart (V7 and V1.5 Gy)\, as well as the ips ilateral lung volume receiving V8 Gy. A two-way ANOVA was performed\, with a significance (p < 0.05).\nResults: VMAT showed superior target coverage \, conformity\, and dose homogeneity compared to t-IMRT across all breast sizes (p < 0.05). PTV D95% coverage with the VMAT plans was 99.8%\, 98.3%\ , and 97.0% for small\, medium\, and large breasts\, respectively\, wherea s the t-IMRT plans failed to achieve the required coverage of ≥95% and r esulted in 89.2-90.0% coverage across all the breast sizes. Also\, the hom ogeneity and conformity indices were improved with the VMAT plan. Both pla ns satisfied the PTV hotspot constraints of D2% < 107% and D5% < 105%. Alt hough the t-IMRT plans resulted in lower hotspot doses for small and mediu m breasts\, the VMAT plans resulted in slightly better hotspot dose contro l for large breasts. However\, the differences were not clinically signifi cant\, as they were within 1%. However\, the t-IMRT plans resulted in supe rior OAR sparing\, with lower ipsilateral lung V8 Gy and lower high-dose c ardiac exposure (Heart V1.5 Gy: 0.9 - 1.1 Gy)\, compared to the VMAT plan s\, which resulted in higher high-dose cardiac exposure (2.9 - 3.3 Gy)\, e xceeding the tolerance limit\, though the VMAT plans resulted in lower low -dose cardiac exposure (Heart V7 Gy).\nConclusion: VMAT provided superior target coverage\, conformity\, and homogeneity across all breasts compared to t-IMRT\, achieving the required PTV D95% ≥ 95% in all cases. Both te chniques were able to meet the PTV hotspot requirements\, with slightly im proved control for larger breasts with VMAT\, while this may not have a cl inically significant impact. However\, t-IMRT showed superior sparing of O ARs\, particularly the ipsilateral lung and high-dose cardiac areas. In co ntrast\, VMAT showed increased dose to high-dose cardiac areas and decreas ed dose to low-dose cardiac areas.\nKeywords: VMAT\, Tangential-IMRT\, Lef t-sided Breast radiotherapy\, Breast sizes\, UK FSAT-Forward protocol.\n\n https://indico.tlabs.ac.za/event/139/contributions/3815/ LOCATION:NRF-iThemba LABS\, Old Faure Road\, Cape Town Auditorium URL:https://indico.tlabs.ac.za/event/139/contributions/3815/ END:VEVENT BEGIN:VEVENT SUMMARY:Computer-Aided Diagnosis of Breast Cancer via Mammography DTSTART;VALUE=DATE-TIME:20260518T122500Z DTEND;VALUE=DATE-TIME:20260518T123300Z DTSTAMP;VALUE=DATE-TIME:20260624T101233Z UID:indico-contribution-643-3806@indico.tlabs.ac.za DESCRIPTION:Speakers: Andrew Lucio Mbewe (Researcher)\n## Advanced Nuclear Science and Technology Techniques (ANSTT6) Workshop\n*18–22 May 2026 — iThemba LABS\, Cape Town*\n\n# Computer-Aided Diagnosis of Breast Canc er via Mammography\n\n**Andrew Lucio Mbewe and Dr. Maluba Vernon Chisapi** \n*University of Zambia\, School of Natural and Applied Sciences\, Lusaka \, Zambia*\n\n---\n\n### Abstract\nZambia faces a critical shortage of rad iologists and limited mammography infrastructure. This has resulted in sig nificant diagnostic delays and in turn to high mortality rates due to late -stage breast cancer presentation [1]. This study aimed to develop and val idate a computer-aided diagnosis (CAD) system utilizing the YOLOv11 deep l earning architecture to automate the detection and classification of breas t cancer lesions in mammograms [2]. A quantitative research design was emp loyed\, using a dataset of 4\,060 anonymized mammograms collected from Mai na Soko Hospital in Lusaka\, Zambia. The model was developed using a progr essive training strategy\, incorporating curriculum learning [3] and utili zed both manual and model-assisted annotation to identify masses\, calcifi cations\, and architectural distortions. The CAD system achieved robust pe rformance results\, reaching an accuracy of **71.4%**\, precision of **72. 2%**\, recall of **70.8%**\, and an F1-score of **71.1%**. While the progr essive training strategy successfully improved detection of underrepresent ed lesions like architectural distortions\, the model faced challenges wit h small lesions and false positive results. These findings demonstrate tha t deep learning-based CAD systems can enhance radiological workflows in re source-limited environments through fast\, automated screening.\n\n**Keywo rds:** Breast Cancer\, YOLOv11\, Computer-Aided Diagnosis (CAD)\, Mammogra phy\, Zambia. \n**Category:** Radiation and Health Physics\n\n---\n\n### References\n1. F. Bray et al.\, “Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 coun tries\,” *CA Cancer J Clin*\, vol. 74\, no. 3\, pp. 229–263\, 2024.\n2 . T. Abd El-Hafeez\, M. A. Shams\, and N. E. Farrag\, “Optimizing YOLOv1 1 for automated classification of breast cancer histopathology images\,” *Scientific Reports*\, vol. 15\, p. 1234\, 2025.\n3. Y. Bengio\, J. Loura dour\, R. Collobert\, and J. Weston\, “Curriculum learning\,” in *Proc . of the 26th Annual Int. Conf. on Machine Learning*\, pp. 41–48\, 2009. \n\nhttps://indico.tlabs.ac.za/event/139/contributions/3806/ LOCATION:NRF-iThemba LABS\, Old Faure Road\, Cape Town Auditorium URL:https://indico.tlabs.ac.za/event/139/contributions/3806/ END:VEVENT END:VCALENDAR