Artificial intelligence for prediction of treatment outcomes in breast cancer: Systematic review of design, reporting standards, and bias.

BACKGROUND: Artificial intelligence (AI) has the potential to personalize treatment strategies for patients with cancer. However, current methodological weaknesses could limit clinical impact. We identified common limitations and suggested potential solutions to facilitate translation of AI to breast cancer management. METHODS: A systematic review was conducted in MEDLINE, Embase, SCOPUS, Google Scholar and PubMed Central in July 2021. Studies investigating the performance of AI to predict outcomes among patients undergoing treatment for breast cancer were included. Algorithm design and adherence to reporting standards were assessed following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. Risk of bias was assessed by using the Prediction model Risk Of Bias Assessment Tool (PROBAST), and correspondence with authors to assess data and code availability. RESULTS: Our search identified 1,124 studies, of which 64 were included: 58 had a retrospective study design, with 6 studies with a prospective design. Access to datasets and code was severely limited (unavailable in 77% and 88% of studies, respectively). On request, data and code were made available in 28% and 18% of cases, respectively. Ethnicity was often under-reported (not reported in 52 of 64, 81%), as was model calibration (63/64, 99%). The risk of bias was high in 72% (46/64) of the studies, especially because of analysis bias. CONCLUSION: Development of AI algorithms should involve external and prospective validation, with improved code and data availability to enhance reliability and translation of this promising approach. Protocol registration number: PROSPERO - CRD42022292495.

Using high density EEG to assess TMS treatment in patients with schizophrenia.

We present preliminary results from the ongoing study entitled "Icelandic AVH-TMS" which aim is to study the effectiveness of repetitive transcranial magnetic stimulation (rTMS) treatment for patients with schizophrenia and with persistent auditory verbal hallucinations (AVH) using symptoms and psychometric scales and high-density EEG system (256 channels). The aim of the present work was to describe cortical topography of the auditory evoked responses like P50 and N100-P300 complex in healthy participants and patients with schizophrenia and to define a robust methodology of signal quantification using dense-array EEG. Preliminary data is shown for three healthy participants and three patients in baseline conditions and for two patients we show the results recorded before and after 10 days rTMS treatment. Our results show differences in sensory gating (P50 suppresion) and a stronger N100-P300 response to rare audio stimulus after the treatment. Moreover we show the value of assessing brain electrical activity from high-density EEG (256 channels) analyzing the results in different regions of interest. However, it is premature and hazardous to assume that rTMS treatment effectiveness in patients with AVH can be assessed using P50 suppression ratio.