Diagnostic challenge and survival analysis of pulmonary oligometastases and primary lung cancer in breast cancer patients.

BACKGROUND: The aim of this study was to compare breast cancer patients with pulmonary oligometastases (POM) and primary lung cancer (PLC) and to assess whether there were differences in clinical features, CT features, and survival outcomes between the two groups. METHODS: From January 2010 to December 2021, the clinical records of 437 with malignant pulmonary nodules who had breast cancer patients were reviewed. POM was identified in 45 patients and PLC in 43 patients after the initial detection of pulmonary nodules. The clinicopathological characteristics, CT appearance of pulmonary nodules, and survival of the two groups were compared. RESULTS: Stage II to IV breast tumors (p < 0.001), high pathological grade of breast cancer (p = 0.001), low proportion of luminal-type breast cancer (p = 0.003), and the higher serum CYFRA 21-1 level (p = 0.046) were the clinical characteristics of pulmonary nodules suggestive of POM rather than PLC. The CT features of lung nodules indicative of PLC rather than POM were the subsolid component (p < 0.001), lobulation (p = 0.010), air bronchogram (p < 0.001) and pleural indentation (p = 0.004). Ten-year survival rate for PLC was 93.2%, which was higher compared with 57.8% in those with POM (p = 0.001). CONCLUSIONS: Elevated serum CYFRA 21-1 levels and late-stage breast cancer may be beneficial for the diagnosis of POM. CT imaging appearances of the subsolid component, lobulation, air bronchogram, and pleural indentation increase the likelihood of PLC. Breast cancer patients with PLC presented better survival with attentive monitoring than those with POM.

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma.

OBJECTIVES: It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC. METHODS: A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model. RESULTS: This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000). CONCLUSION: The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC. CLINICAL RELEVANCE STATEMENT: This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making. KEY POINTS: • It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..

Enhanced fluorescence/magnetic resonance dual imaging and gene therapy of liver cancer using cationized amylose nanoprobe.

Recently, various technologies for targeted gene release in cancer treatment have emerged. However, most of these strategies are facing the challenge of untraceable distribution and poor antitumour treatment effects. In this study, we constructed a gene delivery system that integrated a series of components to assemble multifunctional NPs, providing a promising theranostic nanoplatform for hepatocellular carcinoma (HCC) therapy. Cationized amylose (CA), superparamagnetic iron oxide (SPIO) nanoparticles (NPs), and tetraphenylethylene (TPE) were self-assembled to form nanospheres (CSP/TPE). The prepared NPs was modified with SP94 pepide through amidation reaction, and then survivin small interfering RNA (siRNA) were loaded into the NPs to form CSP/TPE@siRNA-SP94 NPs. Our results showed that the prepared NPs had good size distribution, high RNA condensation and transfection ability. CSP/TPE@siRNA-SP94 NPs exhibited excellent fluorescence and magnetic resonance (MR) imaging properties in vitro and in vivo. The prepared targeted NPs improved Huh-7 cellular uptake in vitro, and the biodistribution of CSP/TPE@siRNA-SP94 in vivo was observed through in/ex vivo fluorescence imaging system and MRI. As survivin siRNA effectively retained in tumour cells, CSP/TPE@siRNA-SP94 NPs considerably inhibited tumour growth in vivo. In addition, H&E staining results showed that all the prepared CSP-based NPs had good biocompatibilities, as few histological changes or tumour metastasis were observed in major organs of the mice in the treatment group. Therefore, we envisage that the prepared CSP/TPE@siRNA-SP94 NPs can represent a promising strategy for HCC diagnosis and treatment.

Imaging and clinical features of colorectal liver metastases with macroscopic intrabiliary growth.

PURPOSE: The purpose of this study was to evaluate imaging and clinical features of colorectal liver metastases complicated with macroscopic intrabiliary growth, and correlate the unusual pattern of spread with treatment and follow-up. METHODS: A retrospective analysis of the clinical, imaging and follow-up files of all patients with surgically resected colorectal liver metastases from January 2016 to October 2020 was reviewed to identify those with macroscopic intrabiliary growth. Two radiologists evaluated the radiological features of colorectal liver metastasis with macroscopic intrabiliary growth. The histopathological findings and follow-up results were also investigated. RESULTS: A total of 555 patients were included. Colorectal liver metastasis with macroscopic intrabiliary growth was present in 5 patients (0.9 %). Four patients experienced tumor recurrence or progression after surgical treatment (80 %), and recurrent tumors retained propensity for intraductal growth. CT (n = 6) and MR (n = 6) examinations were performed before 8 operations with the pathological examination confirmed macroscopic intrabiliary colorectal metastases. According to the location, intrabiliary colorectal metastases were classified into two categories: peripheral (n = 3) and central involvement (n = 5). The lengths of tumoral extension into the downstream bile duct were more than those of extension into the upstream bile duct (P = 0.029). On CT images, all cases showed dilated bile ducts filled with soft tissue attenuation presenting moderate (n = 4) or obvious (n = 2) enhancement. On MR images, all intra-hepatic and intrabiliary components of the metastases showed restricted diffusion on diffusion-weighted imaging, and peritumoral wedge-shaped T1-weighted hyperintensity appeared in the cases with obstruction of intrahepatic bile ducts. CONCLUSIONS: The propensity for colorectal liver metastasis with intrabiliary growth to grow longitudinally and extend beyond the intrahepatic tumor edge elevates the risk of high recurrence after operation. Intrabiliary growth of liver metastasis exhibits characteristic MR and CT imaging features, which help to make an accurate diagnosis and improve treatment plans.