Due to the intrinsic heterogeneity of cancer and variability in individual patient response, personalized nanomedicine based on multi-functional carriers that integrate the functionalities of combination therapy and imaging guidance is highly demanded. Here we developed a multi-functional nanocarrier based on triblock copolymer POEG-b-PVBA-b-PFTS (POVF), which could not only be used for co-delivery of anticancer drugs PTX and Ras inhibitor FTS, but also for PET imaging guided drug delivery. The POVF carrier itself was active in inhibiting the tumor growth in vitro and in vivo. Besides, it was effective in formulating PTX with high drug loading capacity, which further enhanced the tumor inhibition effect. Meanwhile, we developed a simple and universal approach to incorporate a PET radioisotope (Zr-89 and Cu-64) into the azide-containing PTX/POVF micelles via metal-free click chemistry in aqueous solution. The radiolabeled PTX/POVF micelles exhibited excellent serum stability, rapid tumor uptake and slow clearance, which validated the feasibility of the PET image-guided delivery of PTX/POVF micelles.

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We reported the rare case of an elderly man with secondary breast lymphoma (SBL) associated with magnetic resonance imaging findings. MR images demonstrated multiple well-defined masses in the left breast, with heterogeneous enhancement on dynamic contrast-enhanced sequences. The time signal-intensity curve rapidly increased during the initial rise phase and washed out during the delayed phase. The apparent diffusion coefficient (ADC) value was 0.649 × 10 -3 mm 2 /s. Maximum intensity projection (MIP) showed that the masses were distributed in the upper outer quadrant, in the axillary region and in the lower outer region of the left chest wall. The pathology confirmed the diagnosis of non-Hodgkin's lymphoma. The combination of morphological and kinetic features, as well as a significantly lower ADC value, are helpful in the diagnosis of breast lymphoma and its differentiation from breast cancer.

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human–computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human–computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80–88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine.

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