Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.

Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.

Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.

Comparison of Pathologic Tumor Necrosis of Conventional Versus Ultrahypofractionated Preoperative Radiation Therapy in Localized Extremity Soft Tissue Sarcoma and Its Correlation With Clinical Outcomes.

Purpose: We aimed to determine if ultrahypofractionated radiation therapy (UHYPO-RT) delivering 6 Gy x 5 fractions yields similar tumor necrosis compared with conventional radiation therapy (CONV-RT) with 2 Gy x 25 fractions in soft tissue sarcoma. The clinical significance of tumor necrosis on loco-regional recurrence-free survival (LRFS), distant disease-free survival (DDFS), and overall survival (OS) were assessed.

Methods And Materials: Patients with localized soft tissue sarcoma treated with CONV-RT or UHYPO-RT followed by surgery were included.

Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial.

Background: Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoperation risk.

Methods: This study was conducted according to TRIPOD + AI.

A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma.

The objective of this study is to develop a multimodal neural network (MMNN) model that analyzes clinical variables and MRI images of a soft tissue sarcoma (STS) patient, to predict overall survival and risk of distant metastases. We compare the performance of this MMNN to models based on clinical variables alone, radiomics models, and an unimodal neural network. We include patients aged 18 or older with biopsy-proven STS who underwent primary resection between January 1st, 2005, and December 31st, 2020 with complete outcome data and a pre-treatment MRI with both a T1 post-contrast sequence and a T2 fat-sat sequence available.

Distraction Osteogenesis Reconstruction Following Resection of Bone Sarcomas: Surgical, Functional, and Oncological Outcomes from a Prospective Trial Analysis.

Background: While sustainable long-term function has been established for biological reconstruction with distraction osteogenesis (DO) following osseous resections, there is a paucity of published data informing surgeons and patients on important milestones in the reconstructive process. The objectives of this study were to determine when to expect complete bone healing and full weight-bearing as well as to quantify the influence of chemotherapy on the osseous regeneration process.

Methods: Prospectively, pathological and clinical data were collected for 30 consecutive patients who underwent primary or secondary DO-based reconstruction following osseous resection from 2018 to 2021.