Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers

Cipollari, Stefano; Pecoraro, Martina; Forookhi, Ali; Laschena, Ludovica; Bicchetti, Marco; Messina, Emanuele; Lucciola, Sara; Catalano, Carlo; Panebianco, Valeria

doi:10.1007/s11547-022-01555-9

Cited by 14 publications

(10 citation statements)

References 37 publications

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“…As prostate cancer incidence continues to increase, 9 more accurate and automated methods to detect and delineate visible csPCa on MRI to direct MRI-guided biopsies are urgently needed. Deep learning has the potential to detect and locate visible csPCa on mpMRI, [10][11][12][13][14][15][16][17][18][19] but only a few studies 12,16,17 have focused on the detection of visible csPCa in patients with PSA in the range of 4-10 ng/mL. ADC only, T2WI only, or their combination (biparametric) have been studied, and the diagnostic performance shows high variability among centers.…”

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confidence: 99%

Deep‐Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi‐Parametric MRI

Sun

Wu²,

Cui

et al. 2023

Magnetic Resonance Imaging

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BackgroundDeep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate‐specific antigen (PSA) levels of 4–10 ng/mL.PurposeTo explore diffusion‐weighted imaging (DWI), alone and in combination with T2‐weighted imaging (T2WI), for deep‐learning‐based models to detect and localize visible csPCa.Study TypeRetrospective.PopulationOne thousand six hundred twenty‐eight patients with systematic and cognitive‐targeted biopsy‐confirmation (1007 csPCa, 621 non‐csPCa) were divided into model development (N = 1428) and hold‐out test (N = 200) datasets.Field Strength/SequenceDWI with diffusion‐weighted single‐shot gradient echo planar imaging sequence and T2WI with T2‐weighted fast spin echo sequence at 3.0‐T and 1.5‐T.AssessmentThe ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U‐Net. Three radiologists provided the PI‐RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level.Statistical TestsThe performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant.ResultsThe lesion‐level sensitivities of the diffusion model, the biparametric model, and the PI‐RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289–0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant‐level, 0.895 vs. 0.893, P = 0.777; zone‐level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI‐RADS assessment (sextant‐level, 0.734; zone‐level, 0.863).Data ConclusionThe diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4–10 ng/mL.Evidence Level3Technical EfficacyStage 2

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confidence: 99%

Deep‐Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi‐Parametric MRI

Sun

Wu²,

Cui

et al. 2023

Magnetic Resonance Imaging

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Section: Magnetic-resonance Imagingmentioning

confidence: 99%

“…In addition, the combination of morphologic sequences with diffusion sequencing demonstrated a 20% increase in sensitivity by MRI in the identification of lesions [71][72][73][74] (Figures 4 and 5). Indeed, according to Low et al [66], on DWI, ascites and bowel contents are suppressed, while peritoneal and serosal tumors, showing diffusion restriction, are represented as areas of high signal intensity.…”

Section: Magnetic-resonance Imagingmentioning

confidence: 99%

Peritoneal Carcinosis: What the Radiologist Needs to Know

Reginelli,

Giacobbe,

Del Canto

et al. 2023

Diagnostics

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Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.

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“…Combination of morphologic sequences with diffusion sequencing demonstrates a 20% increase in sensitivity by MRI in the identification of lesions [ 31 , 32 , 33 ].…”

Section: Figurementioning

confidence: 99%

Ruptured Ovarian Cystic Teratoma: A Rare Diagnosis, Easily to Be Confused with Peritoneal Carcinomatosis

Costachescu,

Motofelea,

Malita

et al. 2024

Medicina

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Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis.

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Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers

Cited by 14 publications

References 37 publications

Deep‐Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi‐Parametric MRI

Deep‐Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi‐Parametric MRI

Peritoneal Carcinosis: What the Radiologist Needs to Know

Ruptured Ovarian Cystic Teratoma: A Rare Diagnosis, Easily to Be Confused with Peritoneal Carcinomatosis

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