Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans.

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.

A multi-functional polymeric carrier for simultaneous positron emission tomography imaging and combination therapy.

Multifunctional nanoplatforms offering simultaneous imaging and therapeutic functions have been recognized as a highly promising strategy for personalized nanomedicine. In this work, we synthesized a farnesylthiosalicylate (FTS, a nontoxic Ras antagonist) based triblock copolymer POEG-b-PVBA-b-PFTS (POVF) composed of a poly(oligo(ethylene glycol) methacrylate) (POEG) hydrophilic block, a poly(FTS) hydrophobic block, and a poly(4-vinylbenzyl azide) (PVBA) middle block. The POVF polymer itself was active in inhibiting the tumor growth in vitro and in vivo. Besides, it could serve as a carrier to effectively encapsulate paclitaxel (PTX) to form stable PTX/POVF mixed micelles with a diameter around 100 nm. Meanwhile, POVF polymer provides the active azide group for incorporating a positron emission tomography (PET) imaging modality via a facile strategy based on metal-free click chemistry. This nanocarrier system could not only be used for co-delivery of PTX and FTS, but also for PET imaging guided drug delivery. In the 4T1.2 tumor bearing mice, PET imaging showed rapid uptake and slow clearance of radiolabeled PTX/POVF nanomicelles in the tumor tissues. In addition, the FTS-based multi-functional nanocarrier was able to inhibit tumor growth effectively, and the co-delivery of PTX by the carrier further improved the therapeutic effect. STATEMENT OF SIGNIFICANCE: 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.