BackgroundAbnormal activation of PI3K/AKT/mTOR (PAM) pathway, caused by PIK3CA mutation, KRAS mutation, PTEN loss, or AKT1 mutation, is one of the most frequent signaling abnormalities in breast carcinoma. However, distribution and frequencies of mutations in PAM pathway are unclear in breast cancer patients from the mainland of China and the correlation between these mutations and breast cancer outcome remains to be identified.MethodsA total of 288 patients with invasive ductal breast cancer were recruited in this study. Mutations in PIK3CA (exons 4, 9 and 20), KRAS (exon 2) and AKT1 (exon 3) were detected using Sanger sequencing. PTEN loss was measured by immunohistochemistry assay. Correlations between these genetic aberrations and clinicopathological features were analyzed.ResultsThe frequencies of PIK3CA mutation, KRAS mutation, AKT1 mutation and PTEN loss were 15.6%, 1.8%, 4.4% and 35.3%, respectively. However, except for PTEN loss, which was tied to estrogen receptor (ER) status, these alterations were not associated with other clinicopathological features. Survival analysis demonstrated that PIK3CA mutation, PTEN loss and PAM pathway activation were not associated with disease-free survival (DFS). Subgroup analysis of patients with ER positive tumors revealed that PIK3CA mutation more strongly reduced DFS compared to wild-type PIK3CA (76.2% vs. 54.2%; P = 0.011). PIK3CA mutation was also an independent factor for bad prognosis in ER positive patients.Conclusions AKT1, KRAS and PIK3CA mutations and PTEN loss all exist in women with breast cancer in the mainland China. PIK3CA mutation may contribute to the poor outcome of ER positive breast carcinomas, providing evidence for the combination of PI3K/AKT/mTOR inhibitors and endocrine therapy.

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of 99mTc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis.

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Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

Background: Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detection of cancer bone metastasis, and it occupies great workload for nuclear physicians. So we aim to architecture an automatic image interpreting system to assist physicians for diagnosis.Methods: We developed an artificial intelligence (AI) model based on a deep neural network with 12222 cases of 99mTc-MDP bone scintigraphy, and evaluated its diagnostic performance of bone metastases.Results: This AI model demonstrated diagnostic performance by areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians in individually classifying bone metastasis. Further AI-consulting interpretation also improved human diagnostic sensitivity and accuracy.Conclusion: In total, this AI model performed valuable benefit for nuclear physicians in timely and accurate evaluation of cancer bone metastases.