Histopathological Ratios to Predict Gleason Score Agreement between Biopsy and Radical Prostatectomy

Fiorentino, Vincenzo; Martini, Maurizio; Dell’Aquila, Marco; Musarra, Teresa; Orticelli, Ersilia; Larocca, Luigi Maria; Rossi, Ernesto; Totaro, Angelo; Pinto, Francesco; Lenci, Niccolò; Paola, Valerio Di; Manfredi, Riccardo; Bassi, Pier Francesco; Pierconti, Francesco

doi:10.3390/diagnostics11010010

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…On the other hand, the biopsy results might have been affected by the underestimation of the Gleason Score with the diagnosis of PCa. In the future, studies are needed with the length of specimens >12 mm or targeted biopsies to avoid this underestimation [ 34 ]. Fourth, our study did not separate the PZ and TZ lesions due to the small number of enrolled patients.…”

Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Zhang

Tang

et al. 2022

Diagnostics

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Objective: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4–10.0 ng/mL) based on radiomics and other traditional clinical parameters. Methods: In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. Results: The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. Conclusion: The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

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Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Zhang

Tang

et al. 2022

Diagnostics

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“…This combination filtered out minute misclassifications and automatically extracted cancer regions, thereby accurately portraying tumor size and shape. Correct localization would reduce the length of biopsy core to achieve Gleason's score agreement between biopsy and RP specimen and lead to providing accurate information for a decision of active surveillance or treatment [21].…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Localization of Prostate Cancer via Automated Multiparametric MRI Equipped with Artificial Intelligence

Oishi

Kitta

Osawa

et al. 2022

Uro

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Prostate MRI scans for pre-biopsied patients are important. However, fewer radiologists are available for MRI diagnoses, which requires multi-sequential interpretations of multi-slice images. To reduce such a burden, artificial intelligence (AI)-based, computer-aided diagnosis is expected to be a critical technology. We present an AI-based method for pinpointing prostate cancer location and determining tumor morphology using multiparametric MRI. The study enrolled 15 patients who underwent radical prostatectomy between April 2008 and August 2017 at our institution. We labeled the cancer area on the peripheral zone on MR images, comparing MRI with histopathological mapping of radical prostatectomy specimens. Likelihood maps were drawn, and tumors were divided into morphologically distinct regions using the superpixel method. Likelihood maps consisted of pixels, which utilize the cancer likelihood value computed from the T2-weighted, apparent diffusion coefficient, and diffusion-weighted MRI-based texture features. Cancer location was determined based on the likelihood maps. We evaluated the diagnostic performance by the area under the receiver operating characteristic (ROC) curve according to the Chi-square test. The area under the ROC curve was 0.985. Sensitivity and specificity for our approach were 0.875 and 0.961 (p < 0.01), respectively. Our AI-based procedures were successfully applied to automated prostate cancer localization and shape estimation using multiparametric MRI.

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“… 3 Lungs are more vulnerable to inflammatory cytokines than other organs and are the most susceptible target organ, especially in sepsis patients. 4 Acute lung injury (ALI) and severe acute respiratory distress syndrome (ARDS) are serious respiratory diseases that are the main cause of sepsis and the manifestation of sepsis involving the lungs. Despite decades of clinical efforts, mortality in patients with ALI remains high (>50%), and sepsis-related ALI/ARDS has a higher mortality rate compared with ALI induced by other causes.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Enhancement of Isoforskolin and Dexamethasone Against Sepsis and Acute Lung Injury Mouse Models

Fang,

Xiao,

Wang

et al. 2023

JIR

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Background Sepsis is initiated by the dysfunctional response of the host immune system to infection. Septic shock and acute lung injury (ALI) are the main etiology of death caused by sepsis. Glucocorticoids, which are commonly used in clinic to antagonize the inflammatory response of sepsis, may cause serious side effects. Isoforskolin (ISOF) from the plant Coleus forskohlii stimulates adenylyl cyclase, increases the cAMP level and inhibits inflammatory response. The aim of this study was to investigate the synergistic effect of ISOF with dexamethasone (DEX) to prevent and ameliorate septic inflammation. Methods Lipopolysaccharide (LPS) of 30 and 5 mg/kg (iv.) was used to induce sepsis and ALI mice model respectively in vivo. BEAS-2B cells stimulated by LPS were applied as cell model in vitro. The cumulative survival of mice with LPS-induced sepsis and the histopathological changes of lungs in mice with acute lung injury were observed, and the secretion of pro-inflammatory cytokines was analyzed by ELISA. The expression of RGS2 in BEAS-2B cells was detected by immunoblotting assay and PCR. Results In the sepsis mice model, ISOF (10 mg/kg) combined with DEX (10 mg/kg.) (ip.) pretreatment significantly increased mice survival rate from 33.3% to 58.3%, which was significantly higher than that of ISOF or DEX treated alone. In the ALI mice model, ISOF, DEX pretreatment alone and combined application attenuated pulmonary pathological changes in ALI mice. Furthermore, ISOF, DEX alone or combined administration decreased MPO, MDA, IL-6, and IL-8 levels, while significantly synergistic effects were observed in the combined treatment group compared with ISOF or DEX alone. In BEAS-2B cells, combined pretreatment with ISOF and DEX significantly decreased the expression of IL-8 and increased the expression of RGS2. Conclusion The results indicated that ISOF in combination with DEX synergistically improves survival rate and attenuates ALI in mice model through anti-inflammatory and antioxidant effects.

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Histopathological Ratios to Predict Gleason Score Agreement between Biopsy and Radical Prostatectomy

Cited by 8 publications

References 30 publications

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Diagnosis and Localization of Prostate Cancer via Automated Multiparametric MRI Equipped with Artificial Intelligence

Synergistic Enhancement of Isoforskolin and Dexamethasone Against Sepsis and Acute Lung Injury Mouse Models

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