TACE-sorafenib side effects were acceptable, and this treatment may improve overall survival in patients with HCC with first-order or lower-branch PVTT when compared with patients who underwent TACE alone.

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Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1-2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control.

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Background Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. MethodsIn this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

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Background: Accurate lymph nodes (LNs) assessment is important for rectal cancer (RC) staging in multiparametric magnetic resonance imaging (mpMRI). However, it is incredibly time-consumming to identify all the LNs in scan region. This study aims to develop and validate a deep-learning-based, fully-automated lymph node detection and segmentation (auto-LNDS) model based on mpMRI. Methods: In total, 5789 annotated LNs (diameter 3 mm) in mpMRI from 293 patients with RC in a single center were enrolled. Fused T2-weighted images (T2WI) and diffusion-weighted images (DWI) provided input for the deep learning framework Mask R-CNN through transfer learning to generate the auto-LNDS model. The model was then validated both on the internal and external datasets consisting of 935 LNs and 1198 LNs, respectively. The performance for LNs detection was evaluated using sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), and segmentation performance was evaluated using the Dice similarity coefficient (DSC). Findings: For LNs detection, auto-LNDS achieved sensitivity, PPV, and FP/vol of 80.0%, 73.5% and 8.6 in internal testing, and 62.6%, 64.5%, and 8.2 in external testing, respectively, significantly better than the performance of junior radiologists. The time taken for model detection and segmentation was 1.3 s/case, compared with 200 s/ case for the radiologists. For LNs segmentation, the DSC of the model was in the range of 0.81À0.82. Interpretation: This deep learningÀbased auto-LNDS model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.

renal cell carcinoma (RCC), [2] and nonsmall cell lung carcinoma (NSCLC). [3] Through active or passive means, tumor immunotherapy induces the body to produce a tumor-specific immune response which suppresses the tumor progression and prevents the tumor recurrence or metastasis. [4][5][6] Strategies reported for tumor immunotherapy so far mainly involved immune checkpoint blockade (ICB), [7] adoptive cell transfer therapy (ACT), [8] tumor-specific vaccines, [9] and application of small molecular immunemodulating drugs. [10] However, most immunotherapy methods often encounter the great challenge of not being effective enough for many tumors of which a large amount do not respond in the clinic, [11,12] requiring advanced tumor immunotherapy methods to be developed.Among various tumor immunotherapy strategies, inhibition of the indoleamine-2,3-dioxygenase (IDO) is drawing great attention at present because of its critical role in mediating tumor immune evasion, and some inspiring studies were reported. [13][14][15] Admittedly, IDO catalyzes the degradation of amino acid l-tryptophan (Trp) into the metabolite l-kynurenine (Kyn), which suppresses cytotoxic T lymphocytes (CTL) and activates regulatory T (Treg) cells. [16,17] The strong immunomodulatory functions of IDO can be attributed Immunotherapy brings great benefits for tumor therapy in clinical treatments but encounters the severe challenge of low response rate mainly because of the immunosuppressive tumor microenvironment. Multifunctional nanoplatforms integrating effective drug delivery and medical imaging offer tremendous potential for cancer treatment, which may play a critical role in combinational immunotherapy to overcome the immunosuppressive microenvironment for efficient tumor therapy. Here, a nanodrug (BMS-SNAP-MOF) is prepared using glutathione (GSH)-sensitive metal-organic framework (MOF) to encapsulate an immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO) inhibitor BMS-986205, and the nitric oxide (NO) donor s-nitrosothiol groups. The high T1 relaxivity allows magnetic resonance imaging to monitor nanodrug distribution in vivo. After the nanodrug accumulation in tumor tissue via the EPR effect and subsequent internalization into tumor cells, the enriched GSH therein triggers cascade reactions with MOF, which disassembles the nanodrug to rapidly release the IDO-inhibitory BMS-986205 and produces abundant NO. Consequently, the IDO inhibitor and NO synergistically modulate the immunosuppressive tumor microenvironment with increase CD8 + T cells and reduce Treg cells to result in highly effective immunotherapy.In an animal study, treatment using this theranostic nanodrug achieves obvious regressions of both primary and distant 4T1 tumors, highlighting its application potential in advanced tumor immunotherapy.