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The combination of chemotherapy drugs attracts more attention in clinical cancer trials. However, the poor water solubility of chemotherapeutic drugs restricts their anticancer application. In order to improve antitumor efficiency and reduce side effects of free drugs, we prepared paclitaxel (PTX) and honokiol (HK) combination methoxy poly(ethylene glycol)–poly(caprolactone) micelles (P–H/M) by solid dispersion method against breast cancer. The particle size of P–H/M was 28.7±2.5 nm, and transmission electron microscope image confirmed that P–H/M were spherical in shape with small particle size. After being encapsulated in micelles, the release of PTX or HK showed a sustained behavior in vitro. In addition, both the cytotoxicity and the cellular uptake of P–H/M were increased in 4T1 cells, and P–H/M induced more apoptosis than PTX-loaded micelles or HK-loaded micelles, as analyzed by flow cytometry assay and Western blot. Furthermore, the antitumor effect of P–H/M was significantly improved compared with PTX-loaded micelles or HK-loaded micelles in vivo. P–H/M were more effective in inhibiting tumor proliferation, inducing tumor apoptosis, and decreasing the density of microvasculature. Moreover, bioimaging analysis showed that drug-loaded polymeric micelles could accumulate more in tumor tissues compared with the free drug. Our results suggested that P–H/M may have potential applications in breast cancer therapy.

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Background Prostate alignment is subject to interobserver variability in cone-beam CT (CBCT)-based soft-tissue matching. This study aims to analyze the impact of possible interobserver variability in CBCT-based soft-tissue matching for prostate cancer radiotherapy. Methods Retrospective data, consisting of 156 CBCT images from twelve prostate cancer patients with elective nodal irradiation were analyzed in this study. To simulate possible interobserver variability, couch shifts of 2 mm relative to the resulting patient position of prostate alignment were assumed as potential patient positions (27 possibilities). For each CBCT, the doses of the potential patient positions were re-calculated using deformable image registration-based synthetic CT. The impact of the simulated interobserver variability was evaluated using tumor control probabilities (TCPs) and normal tissue complication probabilities (NTCPs). Results No significant differences in TCPs were found between prostate alignment and potential patient positions (0.944 ± 0.003 vs 0.945 ± 0.003, P = 0.117). The average NTCPs of the rectum ranged from 5.16 to 7.29 (%) among the potential patient positions and were highly influenced by the couch shift in the anterior–posterior direction. In contrast, the average NTCPs of the bladder ranged from 0.75 to 1.12 (%) among the potential patient positions and were relatively negligible. Conclusions The NTCPs of the rectum, rather than the TCPs of the target, were highly influenced by the interobserver variability in CBCT-based soft-tissue matching. This study provides a theoretical explanation for daily CBCT-based image guidance and the prostate-rectum interface matching procedure. Trial registration: Not applicable.

Summary A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was whether stereotactic body radiotherapy (SBRT) was equivalent to metastasectomy in patients with pulmonary oligometastases arising from solid tumours. Altogether, 1612 papers were found using the reported search, of which 5 cohort studies derived from 4 patient populations represented the best evidence to answer the clinical question. The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. All 5 studies demonstrated no significant difference in post-treatment overall survival, disease-free survival or local control between SBRT and metastasectomy for pulmonary oligometastases. One of the 5 studies showed a significantly decreased rate of severe complications among the patients treated with SBRT. The other papers reported higher rates of complications in the SBRT groups, invariably due to radiation, but with uncertain clinical significance. The evidence strength of these findings may be largely attenuated due to the small sample size, heterogeneity of SBRT protocols and incomparable follow-up periods between the 2 treatment groups. The selection criteria for the choice of treatment were not stated. We conclude, based on limited evidence, that SBRT has equivalent outcomes to metastasectomy in the treatment of patients with pulmonary oligometastases.

The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients.

Background Artificial intelligence (AI) algorithms are capable of automatically detecting contouring boundaries in medical images. However, the algorithms impact on clinical practice of cervical cancer are unclear. We aimed to develop an AI-assisted system for automatic contouring of the clinical target volume (CTV) and organs-at-risk (OARs) in cervical cancer radiotherapy and conduct clinical-based observations. Methods We first retrospectively collected data of 203 patients with cervical cancer from West China Hospital. The proposed method named as SegNet was developed and trained with different data groups. Quantitative metrics and clinical-based grading were used to evaluate differences between several groups of automatic contours. Then, 20 additional cases were conducted to compare the workload and quality of AI-assisted contours with manual delineation from scratch. Results For automatic CTVs, the dice similarity coefficient (DSC) values of the SegNet trained with incorporating multi-group data achieved 0.85 ± 0.02, which was statistically better than the DSC values of SegNet independently trained with the SegNet(A) (0.82 ± 0.04), SegNet(B) (0.82 ± 0.03) or SegNet(C) (0.81 ± 0.04). Moreover, the DSC values of the SegNet and UNet, respectively, 0.85 and 0.82 for the CTV (P < 0.001), 0.93 and 0.92 for the bladder (P = 0.44), 0.84 and 0.81 for the rectum (P = 0.02), 0.89 and 0.84 for the bowel bag (P < 0.001), 0.93 and 0.92 for the right femoral head (P = 0.17), and 0.92 and 0.91 for the left femoral head (P = 0.25). The clinical-based grading also showed that SegNet trained with multi-group data obtained better performance of 352/360 relative to it trained with the SegNet(A) (334/360), SegNet(B) (333/360) or SegNet(C) (320/360). The manual revision time for automatic CTVs (OARs not yet include) was 9.54 ± 2.42 min relative to fully manual delineation with 30.95 ± 15.24 min. Conclusion The proposed SegNet can improve the performance at automatic delineation for cervical cancer radiotherapy by incorporating multi-group data. It is clinically applicable that the AI-assisted system can shorten manual delineation time at no expense of quality.