Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer.

BACKGROUND: Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors' response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. METHODS: The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. RESULTS: The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). CONCLUSIONS: Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.

Traumatic optic neuropathy: a review.

The aim of this article is to evaluate current literature on investigation and management of traumatic optic neuropathy (TON), propose recommendations for diagnosis and management, and explore novel future treatments. TON, though uncommon, causes substantial visual loss. Without clear guidelines, there is much ambiguity regarding its diagnosis and management. Investigation and treatment (conservative, medical, surgical, and combined) vary widely between centers. Electronic databases PubMed, MEDLINE, PROSPERO, CENTRAL, and EMBASE were searched for content that matched "Traumatic optic neuropathy." Articles with abstracts and full text available, published in the past 10 years, written English and limited to human adults, were selected. All study designs were acceptable except case reports and case series with fewer 10 patients. All abstracts were then evaluated for relevance. References of these studies were evaluated and if also relevant, included. A total of 2,686 articles were retrieved and 43 examined for relevance. Of these, 23 articles were included. TON is a clinical diagnosis. Visual-evoked potential is useful in diagnosis and prognosis. Computed tomography demonstrates canal fractures and concomitant injuries. Magnetic resonance images should be reserved for select and stable patients. Conservative treatment is appropriate in mild TON. Steroids are of questionable benefit and may be harmful. Surgery should be reserved for patients with radiological evidence of compression and individualized.