Browsing by Author "Rigiroli, Francesca"

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    Correlation of pre-operative imaging characteristics with donor outcomes and operative difficulty in laparoscopic donor nephrectomy.
    (American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 2019-09-25) Schwartz, Fides R; Shaw, Brian I; Lerebours, Reginald; Vernuccio, Federica; Rigiroli, Francesca; Gonzalez, Fernando; Luo, Sheng; Rege, Aparna S; Vikraman, Deepak; Hurwitz-Koweek, Lynne; Marin, Daniele; Ravindra, Kadiyala
    This study aimed to understand the relationship of pre-operative measurements and risk factors on operative time and outcomes of laparoscopic donor nephrectomy. 242 kidney donors between 2010 and 2017 were identified. Patient's demographic, anthropomorphic and operative characteristics were abstracted from the electronic medical record. Glomerular filtration rates (GFR) were documented before surgery, within 24 hours, 6, 12 and 24 months after surgery. Standard radiological measures and kidney volumes, subcutaneous and perinephric fat thicknesses were assessed by three radiologists. Data were analyzed using standard statistical measures. There was significant correlation between cranio-caudal and latero-lateral diameters (p<0.0001) and kidney volume. The left kidney was transplanted in 92.6% of cases and the larger kidney in 69.2%. Kidney choice (smaller vs larger) had no statistically significant impact on the rate of change of donor kidney function over time adjusting for age, sex and race (p=0.61). Perinephric fat thickness (+4.08 min) and surgery after 2011 were significantly correlated with operative time (p≤0.01). In conclusion, cranio-caudal diameters can be used as a surrogate measure for volume in the majority of donors. Size may not be a decisive factor for long-term donor kidney function. Perinephric fat around the donor kidney should be reported to facilitate operative planning.
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    CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.
    (Radiology, 2021-09-07) Rigiroli, Francesca; Hoye, Jocelyn; Lerebours, Reginald; Lafata, Kyle J; Li, Cai; Meyer, Mathias; Lyu, Peijie; Ding, Yuqin; Schwartz, Fides R; Mettu, Niharika B; Zani, Sabino; Luo, Sheng; Morgan, Desiree E; Samei, Ehsan; Marin, Daniele
    Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery (SMA) was performed on preoperative CT images with radiomic features extracted to characterize morphology, intensity, texture, and task-based spatial information. The reference standard was the pathologic SMA margin status of the surgical sample: SMA involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm from margin). The preoperative assessment of SMA involvement by a fellowship-trained radiologist in multidisciplinary consensus was the comparison. High reproducibility (intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used to select features included in the logistic regression model. Results A total of 194 patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85 years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved SMA. A total of 1695 CT radiomic features were extracted. The model with five features (maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation, minimum distance, square gray level co-occurrence matrix correlation) showed a better performance compared with the radiologist assessment (model vs radiologist area under the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77) and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.
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    ItemOpen Access
    Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients.
    (European radiology, 2023-04) Schwartz, Fides R; Clark, Darin P; Rigiroli, Francesca; Kalisz, Kevin; Wildman-Tobriner, Benjamin; Thomas, Sarah; Wilson, Joshua; Badea, Cristian T; Marin, Daniele

    Objectives

    Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.

    Methods

    Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.

    Results

    Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.

    Conclusions

    The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.

    Clinical relevance statement

    Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.

    Key points

    • Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.