Bladder cancer is the ninth leading cause of death worldwide and 14th leading cause of death in Pakistan. The objective of this study was to determine the frequency of urothelial carcinoma in various age groups, its gender distribution, and grades. A total of 131 cases of urothelial carcinoma, received at Department of Pathology, Peshawar Medical College, Peshawar, between January 2017 to December 2022, were included in the study; of them 107 (81.6%) were males while 24 (18.3%) were females with a mean age of 62±13 years. The most common histological subtype was papillary urothelial carcinoma in 117(89.3%) cases, followed by Squamous and Glandular in 5(3.8%) cases. Majority of the urothelial carcinoma with high grade showed a statistically significant relation with muscle invasion 38 (50.66%). Males were four times more likely to have urothelial carcinoma while older age groups were more likely to have high grade urothelial carcinoma.

OBJECTIVE: Fertility-sparing treatment (FST) might be considered an option for reproductive patients with low-risk endometrial cancer (EC). On the other hand, the matching rates between preoperative assessment and postoperative pathology in low-risk EC patients are not high enough. We aimed to predict the postoperative pathology depending on preoperative myometrial invasion (MI) and grade in low-risk EC patients to help extend the current criteria for FST.
METHODS: This ancillary study (KGOG 2015S) of Korean Gynecologic Oncology Group 2015, a prospective, multicenter study included patients with no MI or MI <1/2 on preoperative MRI and endometrioid adenocarcinoma and grade 1 or 2 on endometrial biopsy. Among the eligible patients, Groups 1-4 were defined with no MI and grade 1, no MI and grade 2, MI <1/2 and grade 1, and MI <1/2 and grade 2, respectively. New prediction models using machine learning were developed.
RESULTS: Among 251 eligible patients, Groups 1-4 included 106, 41, 74, and 30 patients, respectively. The new prediction models showed superior prediction values to those from conventional analysis. In the new prediction models, the best NPV, sensitivity, and AUC of preoperative each group to predict postoperative each group were as follows: 87.2%, 71.6%, and 0.732 (Group 1); 97.6%, 78.6%, and 0.656 (Group 2); 71.3%, 78.6% and 0.588 (Group 3); 91.8%, 64.9%, and 0.676% (Group 4).
CONCLUSIONS: In low-risk EC patients, the prediction of postoperative pathology was ineffective, but the new prediction models provided a better prediction.

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.

Chromophobe RCC (ChRCC) carries the best prognosis among all RCC subtypes, yet it lacks a proper grading system. Various systems have been suggested in the past, causing much controversy, and Avulova et al. recently proposed a promising four-tier grading system that takes into consideration tumor necrosis. Dysregulation of the mammalian target of the rapamycin (mTOR) pathway plays a key role in ChRCC pathogenesis, highlighting its molecular complexity. The present retrospective study aimed to evaluate the prognostic factors associated with a more aggressive ChRCC phenotype. Materials and Methods: Seventy-two patients diagnosed with ChRCC between 2004 and 2017 were included in our study. Pathology reports and tissue blocks were reviewed, and immunohistochemistry (IHC) was performed in order to assess the expressions of CYLD (tumor-suppressor gene) and mTOR, among other markers. Univariate analysis was performed, and OS was assessed using the Kaplan-Meier method. Results: In our study, 74% of patients were male, with a mean age of 60 years, and the mean tumor size was 63 mm (±44). The majority (54%) were followed for more than 10 years at intervals ranging between 44 and 222 months. The risk of death was significantly higher for patients that were classified as Grade 4 in the Avulova system (HR: 5.83; 95% CI, 1.37-24.7; p: = 0.017). As far as the IHC is concerned, mTOR expression was associated with an HR of 8.57 (95% CI, 1.91-38.5; p = 0.005), and CYLD expression was associated with an HR of 17.3 (95% CI, 1.57-192; p = 0.02). Conclusions: In our study, the Avulova grading system seems to be positively correlated with OS in patients diagnosed with ChRCC. Furthermore, an elevated mTOR expression also shows a negative correlation with OS, whereas an elevated CYLD expression does not seem to exert a protective role. However, because only a small proportion (4.2%) of our patients died due to ChRCC, despite the long follow-up period, the results must be interpreted with caution. Further research is needed to validate our findings.

BACKGROUND: The prognosis of patients with Relapsed/Refractory Osteosarcoma (R/R OS) remains dismal without an agreement on systemic therapy. The use of High-Dose Ifosfamide (14 g/sqm) with an external pump in outpatient setting (14-IFO) in R/R OS patients is limited. This study represents the first retrospective cohort analysis focused on evaluating the activity and toxicity of 14-IFO in this setting.
METHODS: The study investigated 14-IFO activity, in terms of tumour response according to RECIST 1.1 criteria, as well as survival rates and toxicity, according to CTCAE v.5.
RESULTS: The trial enrolled 26 patients with R/R OS. The Overall Response Rate (ORR) and Disease Control Rate (DCR) obtained was 23% and 57.5%, respectively. Patients with relapsed OS showed a higher ORR (45%) and DCR (82%) compared to refractory patients, irrespective of the number of prior treatment lines received. The achievement of disease control with 14-IFO administration enabled 27% of patients to undergo new local treatment. Four-month Progression-Free Survival (PFS) was 54% for all patients and 82% for the relapsed OS sub-group. Median Overall Survival (OSurv) was 13.7 months, with 1-year OSurv of 51% for all patients and 71% for relapsed patients. Age over 18 years and the presence of refractory disease were identified as negative prognostic factors for this patient cohort. A total of 101 cycles were evaluated for toxic assessment, demonstrating a tolerable profile without grade 3-4 non-haematological toxicities.
CONCLUSIONS: 14-IFO should be considered a viable treatment option for R/R OS, particularly due to its well tolerated toxicity profile and the potential for home-administration, which can improve patient quality of life without compromising efficacy.

BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale.
METHODS: In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341.
RESULTS: Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001).
CONCLUSIONS: An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system.
BACKGROUND: Health~Holland and EU Horizon 2020.

BACKGROUND: The TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation) trial assesses the clinical and cost-effectiveness of two biopsy procedures in terms of detection of clinically significant prostate cancer (PCa). This article describes the statistical analysis plan (SAP) for the TRANSLATE randomised controlled trial (RCT).
METHODS: TRANSLATE is a parallel, superiority, multicentre RCT. Biopsy-naïve men aged ≥ 18 years requiring a prostate biopsy for suspicion of possible PCa are randomised (computer-generated 1:1 allocation ratio) to one of two biopsy procedures: transrectal (TRUS) or local anaesthetic transperineal (LATP) biopsy. The primary outcome is the difference in detection rates of clinically significant PCa (defined as Gleason Grade Group ≥ 2, i.e. any Gleason pattern ≥ 4 disease) between the two biopsy procedures. Secondary outcome measures are th eProBE questionnaire (Perception Part and General Symptoms) and International Index of Erectile Function (IIEF, Domain A) scores, International Prostate Symptom Score (IPSS) values, EQ-5D-5L scores, resource use, infection rates, complications, and serious adverse events. We describe in detail the sample size calculation, statistical models used for the analysis, handling of missing data, and planned sensitivity and subgroup analyses. This SAP was pre-specified, written and submitted without prior knowledge of the trial results.
CONCLUSIONS: Publication of the TRANSLATE trial SAP aims to increase the transparency of the data analysis and reduce the risk of outcome reporting bias. Any deviations from the current SAP will be described and justified in the final study report and results publication.
BACKGROUND: International Standard Randomised Controlled Trial Number ISRCTN98159689, registered on 28 January 2021 and registered on the ClinicalTrials.gov (NCT05179694) trials registry.

BACKGROUND: There are currently no standard first-line treatment options for patients with higher grade 2-3, well-differentiated, advanced, gastroenteropancreatic neuroendocrine tumours. We aimed to investigate the efficacy and safety of first-line [177Lu]Lu-DOTA-TATE (177Lu-Dotatate) treatment.
METHODS: NETTER-2 was an open-label, randomised, parallel-group, superiority, phase 3 trial. We enrolled patients (aged ≥15 years) with newly diagnosed higher grade 2 (Ki67 ≥10% and ≤20%) and grade 3 (Ki67 >20% and ≤55%), somatostatin receptor-positive (in all target lesions), advanced gastroenteropancreatic neuroendocrine tumours from 45 centres across nine countries in North America, Europe, and Asia. We used interactive response technologies to randomly assign (2:1) patients to receive four cycles (cycle interval was 8 weeks ± 1 week) of intravenous 177Lu-Dotatate plus intramuscular octreotide 30 mg long-acting repeatable (LAR) then octreotide 30 mg LAR every 4 weeks (177Lu-Dotatate group) or high-dose octreotide 60 mg LAR every 4 weeks (control group), stratified by neuroendocrine tumour grade (2 vs 3) and origin (pancreas vs other). Tumour assessments were done at baseline, week 16, and week 24, and then every 12 weeks until disease progression or death. The primary endpoint was progression-free survival by blinded, independent, central radiology assessment. We did the primary analysis at 101 progression-free survival events as the final progression-free survival analysis. NETTER-2 is registered with ClinicalTrials.gov, NCT03972488, and is active and not recruiting.
RESULTS: Between Jan 22, 2020, and Oct 13, 2022, we screened 261 patients, 35 (13%) of whom were excluded. We randomly assigned 226 (87%) patients (121 [54%] male and 105 [46%] female) to the 177Lu-Dotatate group (n=151 [67%]) and control group (n=75 [33%]). Median progression-free survival was 8·5 months (95% CI 7·7-13·8) in the control group and 22·8 months (19·4-not estimated) in the 177Lu-Dotatate group (stratified hazard ratio 0·276 [0·182-0·418]; p<0·0001). During the treatment period, adverse events (of any grade) occurred in 136 (93%) of 147 treated patients in the 177Lu-Dotatate group and 69 (95%) of 73 treated patients in the control group. There were no study drug-related deaths during the treatment period.
CONCLUSIONS: First-line 177Lu-Dotatate plus octreotide LAR significantly extended median progression-free survival (by 14 months) in patients with grade 2 or 3 advanced gastroenteropancreatic neuroendocrine tumours. 177Lu-Dotatate should be considered a new standard of care in first-line therapy in this population.
BACKGROUND: Advanced Accelerator Applications, a Novartis Company.

BACKGROUND: This study evaluates the efficacy of a nomogram for predicting the pathology upgrade of apical prostate cancer (PCa).
METHODS: A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram\'s performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA).
RESULTS: Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group.
CONCLUSIONS: The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.

BACKGROUND: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics.
METHODS: This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36). CEUS radiographic features were extracted from CEUS images, and XGBoost ML models (US, CP, and MP model) with independent features at different phases were established. Multivariate regression analysis was performed on the characteristics of different radiomics phases to determine the indicators used for developing the prediction model of the combined CEUS model and establishing the XGBoost model. The training set was used to train the above four kinds of radiomics models, which were then tested in the testing set. Radiologists evaluated tumor characteristics, established a CEUS reading model, and compared the diagnostic efficacy of CEUS reading model with independent characteristics and combined CEUS model prediction models.
RESULTS: The combined CEUS radiomics model demonstrated the best performance in the training set, with an area under the curve (AUC) of 0.84, accuracy of 0.779, sensitivity of 0.717, specificity of 0.879, positive predictive value (PPV) of 0.905, and negative predictive value (NPV) of0.659. In the testing set, the AUC was 0.811, with an accuracy of 0.784, sensitivity of 0.783, specificity of 0.786, PPV of 0.857, and NPV of 0.688.
CONCLUSIONS: The radiomics model based on CEUS exhibits high accuracy in non-invasive prediction of ccRCC. This model can be utilized for non-invasive detection of WHO/ISUP nuclear grading of ccRCC and can serve as an effective tool to assist clinical decision-making processes.