MARCELO ARAUJO QUEIROZ
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Câncer do Estado de São Paulo, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/43 - Laboratório de Medicina Nuclear, Hospital das Clínicas, Faculdade de Medicina
LIM/43 - Laboratório de Medicina Nuclear, Hospital das Clínicas, Faculdade de Medicina
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- Estimating 131I biokinetics and radiation doses to the red marrow and whole body in thyroid cancer patients: probe detection versus image quantification(2016) WILLEGAIGNON, José; PELISSONI, Rogério Alexandre; LIMA, Beatriz Christine de Godoy Diniz; SAPIENZA, Marcelo Tatit; COURA-FILHO, George Barberio; QUEIROZ, Marcelo Araújo; BUCHPIGUEL, Carlos AlbertoAbstract Objective: To compare the probe detection method with the image quantification method when estimating 131I biokinetics and radiation doses to the red marrow and whole body in the treatment of thyroid cancer patients. Materials and Methods: Fourteen patients with metastatic thyroid cancer, without metastatic bone involvement, were submitted to therapy planning in order to tailor the therapeutic amount of 131I to each individual. Whole-body scans and probe measurements were performed at 4, 24, 48, 72, and 96 h after 131I administration in order to estimate the effective half-life (Teff) and residence time of 131I in the body. Results: The mean values for Teff and residence time, respectively, were 19 ± 9 h and 28 ± 12 h for probe detection, compared with 20 ± 13 h and 29 ± 18 h for image quantification. The average dose to the red marrow and whole body, respectively, was 0.061 ± 0.041 mGy/MBq and 0.073 ± 0.040 mGy/MBq for probe detection, compared with 0.066 ± 0.055 mGy/MBq and 0.078 ± 0.056 mGy/MBq for image quantification. Statistical analysis proved that there were no significant differences between the two methods for estimating the Teff (p = 0.801), residence time (p = 0.801), dose to the red marrow (p = 0.708), and dose to the whole body (p = 0.811), even when we considered an optimized approach for calculating doses only at 4 h and 96 h after 131I administration (p > 0.914). Conclusion: There is full agreement as to the feasibility of using probe detection and image quantification when estimating 131I biokinetics and red-marrow/whole-body doses. However, because the probe detection method is inefficacious in identifying tumor sites and critical organs during radionuclide therapy and therefore liable to skew adjustment of the amount of 131I to be administered to patients under such therapy, it should be used with caution.
- External validation of a machine learning based algorithm to differentiate hepatic mucinous cystic neoplasms from benign hepatic cysts(2023) FURTADO, Felipe S. S.; BADENES-ROMERO, Alvaro; HESAMI, Mina; MOSTAFAVI, Leila; NAJMI, Zahra; QUEIROZ, Marcelo; MOJTAHED, Amirkasra; ANDERSON, Mark A. A.; CATALANO, Onofrio A. A.Purpose To externally validate an algorithm for non-invasive differentiation of hepatic mucinous cystic neoplasms (MCN) from benign hepatic cysts (BHC), which differ in management. Methods Patients with cystic liver lesions pathologically confirmed as MCN or BHC between January 2005 and March 2022 from multiple institutions were retrospectively included. Five readers (2 radiologists, 3 non-radiologist physicians) independently reviewed contrast-enhanced CT or MRI examinations before tissue sampling and applied the 3-feature classification algorithm described by Hardie et al. to differentiate between MCN and BHC, which had a reported accuracy of 93.5%. The classification was then compared to the pathology results. Interreader agreement between readers across different levels of experience was evaluated with Fleiss' Kappa. Results The final cohort included 159 patients, median age of 62 years (IQR [52.0, 70.0]), 66.7% female (106). Of all patients, 89.3% (142) had BHC, and the remaining 10.7% (17) had MCN on pathology. Agreement for class designation between the radiologists was almost perfect (Fleiss' Kappa 0.840, p < 0.001). The algorithm had an accuracy of 98.1% (95% CI [94.6%, 99.6%]), a positive predictive value of 100.0% (95% CI [76.8%, 100.0%]), a negative predictive value of 97.9% (95% CI [94.1%, 99.6%]), and an area under the receiver operator characteristic curve (AUC) of 0.911 (95% CI [0.818, 1.000]). Conclusion The evaluated algorithm showed similarly high diagnostic accuracy in our external, multi-institutional validation cohort. This 3-feature algorithm is easily and rapidly applied and its features are reproducible among radiologists, showing promise as a clinical decision support tool. [GRAPHICS] .
- Multicenter External Validation of a Nomogram for Predicting Positive Prostate-specific Membrane Antigen/Positron Emission Tomography Scan in Patients with Prostate Cancer Recurrence(2023) BIANCHI, Lorenzo; CASTELLUCCI, Paolo; FAROLFI, Andrea; DROGHETTI, Matteo; ARTIGAS, Carlos; LEITE, Jose; CORONA, Paola; SHAGERA, Qaid Ahmed; MOREIRA, Renata; GONZALEZ, Christian; QUEIROZ, Marcelo; BARBOSA, Felipe de Galiza; SCHIAVINA, Riccardo; DEANDREIS, Desiree; FANTI, Stefano; CECI, FrancescoBackground: A nomogram has recently been developed to predict 68Ga-labeled prostate-specific membrane antigen (PSMA)-11 positron emission tomography (PET)/computed tomography (PSMA-PET) results in recurrent prostate cancer (PCa) patients.Objective: To perform external validation of the original nomogram in a multicentric set-ting.Design, setting, and participants: A total of 1639 patients who underwent PSMA-PET for prostate-specific antigen (PSA) relapse after radical therapy were retrospectively included from six high-volume PET centers. The external cohort was stratified according to clinical setting categories: group 1: first-time biochemical recurrence (n = 774); group 2: PSA relapse after salvage therapy (n = 499); group-3: biochemical persistence after rad-ical prostatectomy (n = 210); and group-4: advanced-stage PCa before second-line sys-temic therapies (n = 124).Intervention: PSMA-PET in recurrent PCa.Outcome measurements and statistical analysis: PSMA-PET detection rate was assessed in the overall population and in each subgroup. A multivariable logistic regression model was produced to evaluate the predictors of a positive scan. The performance characteris-tics of the model were assessed by quantifying the predictive accuracy (PA) according to model calibration. The Youden's index was used to find the best nomogram's cutoff. Decision curve analysis (DCA) was implemented to quantify the nomogram's clinical net benefit.Results and limitations: In the external cohort, the overall detection rate was 53.8% ver-sus 51.2% in the original population. At multivariate analysis, International Society of Urological Pathology grade group, PSA, PSA doubling time, and clinical setting were inde-pendent predictors of a positive scan (all p < 0.02). The PA of the nomogram was identical to the original model (82.0%); the model showed an optimal calibration curve. The best nomogram's cutoff was 55%. In the DCA, the nomogram revealed clinical net benefit when the threshold nomogram probabilities were >= 20%. The retrospective design is a major limitation.Conclusions: The original nomogram exhibited excellent characteristics on external val-idation. The incidence of a false negative scan can be reduced if PSMA-PET is performed when the predicted probability is >= 20%.Patient summary: A nomogram has been developed to predict prostate-specific mem-brane antigen/positron emission tomography (PSMA-PET) results for recurrent prostate cancer (PCa). The nomogram represents an easy tool in the decision-making process of recurrent PCa.(c) 2021 European Association of Urology.
- Defining clinical workflow and PSMA PET/MR protocol for prostate cancer evaluation: Initial experience and results(2019) QUEIROZ, M. A.; FERRARO, D. A.; BUCHPIGUEL, C. A.; CERRI, G. G.Purpose.-To evaluate the clinical workflow and the PET/MR protocol of prostate cancer (PCa) evaluation according to the indication. Methods.-Eleven patients underwent to PSMA PET/MR both for primary staging (PS) and assessment of biochemical recurrence (BCR) of PCa. The clinical workflow was evaluated regarding patient preparation, total uptake time and total acquisition time. The PET/MR protocol was defined according to indication, using a biparametric MRI for PS and a multiparametric MRI for BCR. One reader analyzed the PET/MR for TNM lesion in the PS setting and for locoregional and distant metastasis in the BCR patients. Results.-Four patients (36%) were included for PS and 7 (64%) for assessing BCR of prostate cancer. PSA levels ranged from 0.26 to 33.94 ng/mL and the most prevalent ISUP grade score was 3 (4/11 patients). The average total scan time when including DCE-MR was 73 minutes versus 64 minutes when performing biparametric MR. Nine out of the eleven (82%) patients presented positive findings on PSMA PET/MR. The most prevalent site of disease was local (primary or recurrent tumor) in 6/11 patients (55%) followed by regional lymph nodes in 4/11 (36%). In primary staging patients, the PCa index lesion was depicted in all four patients. In BCR patients, five out of seven patients had positive PET/MR scans. Conclusion.-PET/MRI for prostate cancer evaluation is feasible with a tolerable scan time and, although very preliminary, it reinforces the high detection rate of PSMA PET imaging both for PS and for assessment of BCR.