Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer. METHODS: In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer. FINDINGS: Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values. INTERPRETATION: The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer. FUNDING: Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Discriminative fusion of moments-aligned latent representation of multimodality medical data.

Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling.

STMN1 as a novel prognostic biomarker in HCC correlating with immune infiltrates and methylation.

BACKGROUND: Upregulation of Stathmin 1 (STMN1), a cytoplasmic phosphoprotein that controls the dynamics of cellular microtubules, is linked to malignant behavior and poor prognosis in a range of malignancies. However, little research has been done on STMN1's potential role in HCC as a single factor in DNA methylation, m6A, or immunological modulation. RESULTS: STMN1 is overexpressed in hepatocellular carcinoma, where it is related to clinicopathological parameters and affects the prognosis of HCC patients. STMN1 overexpression plays an important role in the diagnosis and prognosis of hepatocellular carcinoma. Meanwhile, methylation of 7 CpG sites of STMN1 in HCC was correlated with prognosis, and STMN1 expression was closely related to m6A modification. In addition, STMN1 expression is associated with immune cell infiltration, immune molecules, and immune checkpoints in HCC. CONCLUSION: STMN1 has a significant role in hepatocellular carcinoma diagnosis and prediction. STMN1 is implicated not just in the onset and course but also in the immunological modulation of the disease. DNA methylation and m6A are both linked to STMN1. Therefore, STMN1 could be used as a diagnostic and prognostic biomarker for HCC, as well as a target for immunotherapy.