Sclerotherapy for hemorrhoidal disease: systematic review and meta-analysis.

BACKGROUND: This systematic review and meta-analysis aimed to evaluate the safety and efficacy of sclerotherapy methods for hemorrhoidal disease (HD) over the past 40 years. METHODS: The review followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A comprehensive literature search was conducted, including studies reporting the use of sclerotherapy in patients with HD. Study eligibility criteria were defined, and data were extracted independently by the authors. Random-effects meta-analyses were performed to assess outcomes of interest. RESULTS: Out of 1965 records identified, 44 studies met the inclusion criteria, involving 9729 patients. The majority of studies were conducted in Japan, followed by the UK, Italy, and Portugal. The median age of participants was 52 years, and the majority were male. The Goligher grade distribution indicated varying degrees of HD severity. Sclerotherapy was predominantly administered through anoscopy, with polidocanol being the most commonly used agent. The procedure was generally performed without pre-injection analgesia. The meta-analysis of 14 randomized controlled trials (RCTs) revealed that sclerotherapy was not inferior to control interventions in terms of success rate (risk ratio [RR] 1.00, 95% CI 0.71-1.41) and recurrence rate (RR 1.11, 95% CI 0.69-1.77), while resulting in fewer complications (RR 0.46, 95% CI 0.23-0.92). CONCLUSIONS: This systematic review highlights the safety and efficacy of sclerotherapy for HD, which yields similar success rates and fewer complications compared to other conservative or surgical approaches. Further research is warranted to optimize sclerotherapy techniques and evaluate long-term outcomes. REGISTRATION: PROSPERO 2023 CRD42023396910.

Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma.

BACKGROUND: The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. METHODS: CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. RESULTS: Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. CONCLUSION: Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.