9 research outputs found

    Distant Metastases From Childhood Differentiated Thyroid Carcinoma:Clinical Course and Mutational Landscape

    Get PDF
    Context: Distant metastases (DM) from childhood differentiated thyroid carcinoma (DTC) are uncommon and published studies are limited. Objective: This work aimed to describe the outcomes of patients with DM from childhood DTC and to evaluate the molecular landscape of these tumors. Methods: A retrospective study was conducted at a tertiary cancer center including patients with pediatric DTC (diagnosed at age Results: We identified 148 patients; 144 (97%) had papillary thyroid carcinoma (PTC) and 104 (70%) were female. Median age at DTC diagnosis was 13.4 years (interquartile range [IQR], 9.9-15.9 years). Evaluable individuals received a median of 2 (IQR, 1-3) radioactive iodine (RAI) treatments at a median cumulative administered activity of 238.0 mCi (IQR, 147.5-351.0 mCi). The oncogenic driver was determined in 64 of 69 PTC samples: RET fusion (38/64; 59%), NTRK1/3 fusions (18/64; 28%), and the BRAF V600E mutation (8/64; 13%). At last evaluation, 93% had persistent disease. The median overall and disease-specific survival after DTC diagnosis were 50.7 and 52.8 years, respectively. Eight (5%) PTC patients died of disease after a median of 30.7 years (IQR, 20.6-37.6 years). Conclusion: Childhood DTC with DM persists in most patients despite multiple courses of RAI, but disease-specific death is uncommon, typically occurring decades after diagnosis. Fusion genes are highly prevalent in PTC, and all identified molecular alterations have appropriate targeted therapies. Future studies should focus on expanding genotype-phenotype correlations, determining how to integrate molecularly targeted therapy into treatment paradigms, and relying less on repeated courses of RAI to achieve cure in patients with DM from childhood DTC

    Prediction of Liver Regeneration Post-Radiotherapy Using Machine Learning and Deep Learning Models

    Get PDF
    Department of Imaging Physics Radiation Oncology Interventional Radiologyhttps://openworks.mdanderson.org/sumexp22/1136/thumbnail.jp

    Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

    No full text
    Abstract Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ( MpaU−Net){{\text{M}}}_{{\text{paU}}-{\text{Net}}}) M paU - Net ) and 3d full resolution of nnU-Net ( MnnU−Net){{\text{M}}}_{{\text{nnU}}-{\text{Net}}}) M nnU - Net ) to determine the best architecture ( BA){\text{BA}}) BA ) . BA was used with vessels ( MVess){{\text{M}}}_{{\text{Vess}}}) M Vess ) and spleen ( Mseg+spleen){{\text{M}}}_{{\text{seg}}+{\text{spleen}}}) M seg + spleen ) to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ( CRTTrain{{\text{C}}}_{{\text{RTTrain}}} C RTTrain ), 40 ( CRTVal{{\text{C}}}_{{\text{RTVal}}} C RTVal ), 33 ( CLS{{\text{C}}}_{{\text{LS}}} C LS ), 25 (CCH) and 20 (CPVE) CECT of LC patients. MnnU−Net{{\text{M}}}_{{\text{nnU}}-{\text{Net}}} M nnU - Net outperformed MpaU−Net{{\text{M}}}_{{\text{paU}}-{\text{Net}}} M paU - Net across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03–0.05 (p  0.05), however, both were slightly better than MVess{{\text{M}}}_{{\text{Vess}}} M Vess by DSC up to 0.02. The final model, Mseg+spleen{{\text{M}}}_{{\text{seg}}+{\text{spleen}}} M seg + spleen , showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5–8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score ≥\ge ≥ 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning
    corecore