Purpose: To evaluate the risk of pneumothorax in the percutaneous image-guided thermal ablation (IGTA) treatment of colorectal lung metastases (CRLM). Methods: Data regarding patients with CRLM treated with IGTA from five medical institutions in China from 2016 to 2023 were reviewed retrospectively. Pneumothorax and non-pneumothorax were compared using the Student’s t test、Chi-square test and Fisher’s exact test. Univariate logistic regression analysis was conducted to identify potential risk factors, followed by multivariate logistic regression (MLR) analysis to evaluate the predictors of pneumothorax. Interactions between variables were examined and used for model construction. Receiver operating characteristic (ROC) curves and nomograms were generated to assess the performance of the model. Results: A total of 254 patients with 376 CRLM underwent 299 ablation sessions. The incidence of pneumothorax was 45.5%. The adjusted MLR model, incorporating interaction terms, revealed that tumor number (odds ratio [OR]=8.34 [95% confidence interval [CI]: 1.37-50.64]), puncture depth (OR=0.53 [95%CI: 0.31-0.91]), pre-procedure radiotherapy (OR=3.66 [95%CI: 1.17-11.40]), peri-bronchial tumor (OR=2.32 [95%CI: 1.04-5.15]), and emphysema (OR=56.83 [95%CI: 8.42-383.57]) were significant predictive factors of pneumothorax (all P<0.05). The generated nomogram model demonstrated a significant prediction performance, with an area under the ROC curve of 0.800 (95%CI: 0.751-0.850). Conclusions: Pre-procedure radiotherapy, tumor number, peri-bronchial tumor, and emphysema were identified as risk factors for pneumothorax in the treatment of CRLM using percutaneous IGTA. Puncture depth was found to be a protective factor against pneumothorax.

BackgroundThe novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models’ generalization ability.MethodsWe retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.ResultsThe AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).ConclusionThe nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Background This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Abstract Background The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground‐glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. Methods We developed and validated a three‐dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre‐training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end‐to‐end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. Results The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). Conclusions Our 3D deep transfer learning model provides a noninvasive, low‐cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

Traditional drug chemotherapy reveals numerous unsatisfactory aspects for malignant tumors that is prone to recurrence and metastasis, such as low therapy effect, systemic side effects, and multidrug resistance. Based on the above considerations, we herein propose a non-drug chemotherapy-like in synergy with gas-/immunotherapy strategy based on tumor cell membrane (M) coating Ferulic acid (FA)-L-Arg-ovalbumin (OVA) nanoparticles (F-L-O@M NPs). It is found that the non-drug of FA can efficiently induce cancer cell apoptosis via the Janus kinases/signal transducer and activator of transcription 3 (JAK/STAT3) signal pathway. Moreover, the generation of NO gas ascribed to the oxidation of L-Arg by H2O2, not only restricts the respiratory metabolism of mitochondria and reduces the generation of ATP, but also downregulates the expression of P-gp protein, exhibiting a superior synergistic with FA-mediated antineoplastic. More importantly, NO gas augments FA-induced tumor cell apoptosis and efficiently release tumor-associated antigens, which in synergy with OVA enables significant activation and recruits cytotoxic T lymphocytes to infiltrate in tumor tissues, further combining with immune checkpoint blockade antibody to trigger a long-term immune memory response, as well as anti-metastasis/recurrence. It is highly expected that such a non-drug chemotherapy-like strategy that combines with gas-/immunotherapy showing great promising in the clinical translational potential of nanomedicine.

Overproduced hydrogen sulfide (H2S) is a highly potential target for precise colorectal cancer (CRC) therapy; herein, a novel 5-Fu/Cur-P@HMPB nanomedicine is developed by coencapsulation of the natural anticancer drug curcumin (Cur) and the clinical chemotherapeutic drug 5-fluorouracil (5-Fu) into hollow mesoporous Prussian blue (HMPB). HMPB with low Fenton-catalytic activity can react with endogenous H2S and convert into high Fenton-catalytic Prussian white (PW), which can generate in situ a high level of •OH to activate chemodynamic therapy (CDT) and meanwhile trigger autophagy. Importantly, the autophagy can be amplified by Cur to induce autophagic cell death; moreover, Cur also acted as a specific chemosensitizer of the chemotherapy drug 5-Fu, achieving a good synergistic antitumor effect. Such a triple synergistic therapy based on a novel nanomedicine has been verified both in vitro and in vivo to have high efficacy in CRC treatment, showing promising potential in translational medicine.

Magnetic hyperthermia and reactive oxygen species (ROS)-related nanocatalytic tumor therapy has received increasing focus due to their no penetration depth limit, tumor-specificity, and non-invasiveness. However, their efficacy is still affected by the intrinsic protective mechanism of cells' autophagy. Herein, a novel mesoporous magnetic copper ferrite nanoagent (CuFe2O4 NPs) containing autophagy inhibitor chloroquine was developed for the synergistic enhancement of cancer treatment. Specifically, the mesoporous structured CuFe2O4 NPs with high surface area was endowed with highly efficient ROS generation via self-cycling Fenton redox process between Cu2+ and Fe3+. More importantly, the inside autophagy inhibitor chloroquine could efficiently inhibit cancer cells' resistance to hyperthermia and oxidative stress, realizing mild magnetic hyperthermia therapy (MHT) under 45 & DEG;C and preventing from damages to normal tissues. Furthermore, the temperature rise within tumor area further accelerated the Fenton reaction to boost the productivity of hydroxyl radicals (center dot & nbsp;OH) for enhancement of chemodynamic therapy (CDT). This work offers a novel strategy of magnetothermal-augmented in-situ self-cycling redox nanocatalysis in synergy with inhibition of autophagy, enlightening a new insight on the design of multifunctional therapeutic nanoagents based on a single component. (c) 2022 Elsevier Ltd. All rights reserved.

The immunosuppressive tumor microenvironment (TME) always causes poor antitumor immune efficacy, prone to relapse and metastasis. Herein, novel poly(vinylpyrrolidone) (PVP) modified BiFeO3 /Bi2 WO6 (BFO/BWO) with a p-n type heterojunction is constructed for reshaping the immunosuppressive TME. Reactive oxygen species can be generated under light activation by the well-separated hole (h+ )-electron (e- ) pairs owing to the heterojunction in BFO/BWO-PVP NPs. Interestingly, h+ can trigger the decomposition of H2 O2 to generate O2 for alleviating tumor hypoxia, which not only sensitizes photodynamic therapy (PDT) and radiotherapy (RT), but also promotes tumor-associated macrophages (TAMs) polarization from M2 to M1 phenotype, which is beneficial to decrease the expression of HIF-1α. Importantly, such a light-activated nanoplatform, combining with RT can efficiently activate and recruit cytotoxic T lymphocytes to infiltrate in tumor tissues, as well as stimulate TAMs to M1 phenotype, dramatically reverse the immunosuppressive TME into an immunoactive one, and further boost immune memory responses. Moreover, BFO/BWO-PVP NPs also present high performance for computed tomography imaging contrast. Taken together, this work offers a novel paradigm for achieving O2 self-supply of inorganic nanoagents and reshaping of the tumor immune microenvironment for effective inhibition of cancer as well as metastasis and recurrence.

Abstract Inorganic nanoplatform exhibits great potentials in drug delivery and responsive release attributed to its inherent physicochemical properties, good biocompatibility, surface modification, and easy synthesis. In this review, the recent progresses on the inorganic smart bio‐responsive nanoplatforms for tumor theranostics are summarized, which could be triggered by either endogenous tumor microenvironment (TME) or the exogenous physical and hopeful to achieve safe, precise, and high efficacy for tumor therapy. Notably, these nanoplatforms generally are dependent on the intelligent and multifunctional design of nanocarriers, including mesoporous silica nanoparticles (MSNs), black phosphorus (BP), Prussian blue (PB), and other inorganic‐based nanoparticles. Finally, the perspectives and challenges of inorganic nanoplatform in the future translational medicine are proposed.

Gas therapy (GT) exhibits great potential for clinical application due to its high therapeutic efficiency, low systemic side effects, and biosafety, thereinto, a multifunctional nanoplatform is generally needed for controllable gas release and precise delivery to tumor tissue. In this review, the recent development of multifunctional nanoplatforms for efficient tumor delivery of stimuliresponsive gas-releasing molecules (GRMs), which could be triggered by either exogenous physical or endogenous tumor microenvironment (TME) is summarized. The reported therapeutic gas molecules, including oxygen (O-2), hydrogen sulfide (H2S), nitric oxide (NO), hydrogen (H-2), and carbon monoxide (CO), etc., could directly influence or change the pathological status. Additionally, abundant nanocarriers have been employed for gas delivery into cancer region, such as mesoporous silica nanoparticles (MSNs), metal-organic frameworks (MOFs), two-dimensional (2D) nanomaterials, and liposomes, as well as non-nanocarriers including inorganic and organic nanoparticles. In the end, the outlooks of current challenges of GT and GRMs delivery nanoplatforms as well as the prospects of future clinical applications are proposed.

Purpose To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs).MethodsWe retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect.ResultsThere was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities.ConclusionsThe radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.

In article number 2007991, Yu Luo, Hangrong Chen, and co-workers report a novel kind of polyvinyl pyrrolidone modified multifunctional iron sulfide nanoparticles (Fe1−xS-PVP NPs), which can in situ generate highly toxic hydroxyl radicals under a tumor environment and simultaneously produce H2S gas to suppress the activity of the enzyme cytochrome c oxidase in cancer cells. The external stimulation of 808 nm near-infrared irradiation will further facilitate this process.

Redox homeostasis is vital for cell survival. Nowadays, developing novel nanoagents that can efficiently break the redox homeostasis, which includes improving the reactive oxygen species level while reducing the glutathione (GSH) level, has emerged as a promising but challenging strategy for tumor therapy. In this work, a novel albumin‐based multifunctional nanoagent is developed for GSH‐depletion assisted chemo‐/chemodynamic combination therapy. Briefly, CuO and MnO X are in situ co‐grown inside the albumin molecules through a facile biomineralization process, followed by the conjugation of Pt (IV) prodrug to obtain the final nanoagent. Thereinto, copper species can produce •OH with optimal efficiency under weakly acidic conditions (pH = 6.5), while MnO X can react with GSH, leading to the GSH depletion, which reduces the formation of GSH‐Pt adducts and •OH consumption, thus favoring a better chemotherapy and chemodynamic therapy effect, respectively. Significantly, both GSH depletion and •OH generation contributes to the inhibited expression of GPX‐4, which further increases the oxidative stress. Moreover, during the reaction between MnO X and GSH or H 2 O 2 , Mn 2+ ions are released for MR imaging while O 2 is produced for hypoxia relief. It is believed that the proposed strategy can provide a new perspective on effective tumor therapy.

Nanocatalytic medicine has emerged as a promising method for the specific cancer therapy by mediating the interaction between tumor microenvironment biomarkers and nanoagents. However, the produced antitumor cell killing molecules, such as reactive oxygen species (ROS), by catalysis are insufficient to inhibit tumor growth. Herein, a novel kind of polyvinyl pyrrolidone modified multifunctional iron sulfide nanoparticles (Fe 1− x S‐PVP NPs) is developed via a one‐step hydrothermal method, which exhibits high photothermal (PT) conversion efficiency (η = 24%) under the irradiation of 808 nm near‐infrared laser. The increased temperature further facilitates the Fenton reaction to generate abundant •OH radicals. More importantly, under an acidic (pH = 6.5) condition within tumor environment, the Fe 1− x S‐PVP NPs can in situ produce H 2 S gas, which is evidenced to suppress the activity of enzyme cytochrome c oxidase (COX IV) in cancer cells, contributing to inhibit the growth of tumor. Both in vitro and in vivo results demonstrate that the H 2 S‐mediated gas therapy in combination with PT enhanced ROS achieves excellent antitumor performance, which can open up a new approach for the design of gas‐mediated cancer treatment.

血管母细胞瘤又称毛细血管性血管母细胞瘤、血管网状细胞瘤或毛细胞血管内皮细胞瘤,是由于中胚叶形成的血管细胞的真性肿瘤,好发于小脑,幕上少见,椎管内硬膜外血管母细胞瘤罕见.现报告1例椎管内硬膜外实性血管母细胞瘤,通过回顾文献,结合分析本例影像及临床诊治过程,增强对该肿瘤的认识,为该病的诊治提供参考.

In this study, an organic semiconducting pro-nanostimulant (OSPS) with a near-infrared (NIR) photoactivatable immunotherapeutic action for synergetic cancer therapy is presented. OSPS comprises a semiconducting polymer nanoparticle (SPN) core and an immunostimulant conjugated through a singlet oxygen (O-1(2)) cleavable linkers. Upon NIR laser irradiation, OSPS generates both heat and O-1(2) to exert combinational phototherapy not only to ablate tumors but also to produce tumor-associated antigens. More importantly, NIR irradiation triggers the cleavage of O-1(2)-cleavable linkers, triggering the remote release of the immunostimulants from OSPS to modulate the immunosuppressive tumor microenvironment. Thus, the released tumor-associated antigens in conjunction with activated immunostimulants induce a synergistic antitumor immune response after OSPS-mediated phototherapy, resulting in the inhibited growth of both primary/distant tumors and lung metastasis in a mouse xenograft model, which is not observed for sole phototherapy.