PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

Rationale: Granulosa cell tumors (GCTs) are rare, hormonally active sex cord-stromal tumors that generally present as solid unilateral ovarian lesions. It's quite uncommon that they present as pure bilateral ovarian cysts. Histopathology remains the gold standard for making a diagnosis of GCTs. However, as the differential diagnosis is difficult, cystic GCTs are frequently misdiagnosed as benign or other cystic tumors either prior to surgery or during pathologic diagnosis. Accordingly, herein, we describe a fairly rare case of bilateral ovarian cystic GCTs, along with a review of the related literature. Patient concerns: A 43-year-old woman presented with abdominal distension and chronic pain since 1 day. The patient had a history of dysmenorrhea. Diagnoses: Physical examination revealed palpable bilateral adnexal tumors; ultrasonography revealed cystic and septate masses with a maximum diameter of 7.8 and 10.7 cm, respectively, in the bilateral ovaries. Hormonal analysis revealed that the blood estradiol levels were elevated. Postoperative pathological and immunohistochemical examinations of the surgical specimens revealed a final diagnosis of cystic adult GCTs of the ovaries. Interventions: The patient first underwent laparoscopic bilateral ovarian cystectomy. On the basis of the final pathological diagnosis report, abdominal total hysterectomy, bilateral oophoro-salpingectomy, and partial omentectomy were then performed. Microscopic examination revealed that there were no residual CGT cells. The patient's federation international of gynecology and obstetrics (FIGO) Stage was IB period. Outcomes: The surgeries were successful. The tumor was a FIGO Stage IB tumor, and the patient did not require any additional treatment. The patient had been followed-up regularly for 2 years after surgery; she did not experience any complications and remained disease-free. Lessons subsections: Cystic GCTs should be considered in the differential diagnosis if a female patient shows bilateral ovarian cysts. They are extremely rare ovarian malignant tumors that must be differentiated from other ovarian tumors, especially purely cystic tumors and benign cysts. Although pathological and immunohistochemical findings are important for making the diagnosis, the varying histopathological features on microscope make diagnosis difficult, including tumor cells with luteinization or free cell clusters. The current case highlights the importance of physicians being aware of and suspecting cystic CGTs in similar cases, along with knowing the characteristics of GCTs for the diagnosis and differential diagnosis.

Purpose: Clinical responses of neoadjuvant chemotherapy (NACT) are associated with prognosis in patients with breast cancer. The selection of suitable variables for the prediction of clinical responses remains controversial. Herein, we developed a predictive model based on ultrasound imaging and clinical indices to identify patients most likely to benefit from NACT. Patients and Methods: We recruited a total of 225 consecutive patients who underwent NACT followed by surgery and axillary lymph node dissection at the Sixth Hospital of Ning Bo City of Zhe Jiang Province between January 1, 2018, and March 31, 2021. All patients had been diagnosed with breast cancer following the clinical examination. First, we created a training cohort of patients who underwent NACT+surgery (N=180) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+ surgery (N=45). Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to NACT; these were then incorporated into the nomogram. Results: Multivariate logistic regression analysis identified several significant differences as to clinical responses of NACT, including neutrophil-lymphocyte ratio (NLR), body mass index (BMI), pulsatility index (PI), resistance index (RI), blood flow, Ki67, histological type, molecular subtyping, and tumor size. The performance of the nomogram score exhibited a robust C-index of 0.89 (95% confidence interval [CI]: 0.83 to 0.95) in the training cohort and a high C-index of 0.87 (95% CI: 0.81 to 0.93) in the validation cohort. Clinical impact curves showed that the nomogram had a good predictive ability. Conclusion:We successfully established an accurate and optimized nomogram incorporated ultrasound imaging and clinical indices that could be used preoperatively to predict clinical responses of NACT. This model can be used to evaluate the risk of clinical responses to NACT and therefore facilitate the choice of personalized therapy.

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