A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study

Using Machine Learning to Select Breast Implant Volume

BACKGROUND

In breast augmentation surgery, selection of the appropriate breast implant size is a crucial step that can greatly affect patient satisfaction and the outcome of the procedure. However, this decision is often based on the subjective judgment of the surgeon and the patient, which can lead to suboptimal results. The authors aimed to develop a machine-learning approach that can accurately predict the size of breast implants selected for breast augmentation surgery.

METHODS

The authors collected data on patient demographic characteristics, medical history, and surgeon preferences from a sample of 1000 consecutive patients who underwent breast augmentation. This information was used to train and test a supervised machine-learning model to predict the size of breast implant needed.

RESULTS

The study demonstrated the effectiveness of the algorithm in predicting breast implant size, achieving a Pearson correlation coefficient of 0.9335 ( P < 0.001). The model generated accurate predictions in 86% of instances, with a mean absolute error of 27.10 mL. Its effectiveness was confirmed in the reoperation group, in which 36 of 57 patients (63%) would have received a more suitable implant size if the model's suggestion had been followed, potentially avoiding reoperation.

CONCLUSIONS

The findings show that machine learning can accurately predict the needed size of breast implants in augmentation surgery. By integrating the artificial intelligence model into a decision support system for breast augmentation surgery, essential guidance can be provided to surgeons and patients. This approach not only streamlines the implant selection process but also facilitates enhanced communication and decision-making, ultimately leading to more reliable outcomes and improved patient satisfaction.

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.