Although boron neutron capture therapy (BNCT) is a promising treatment option for malignant brain tumors, the optimal BNCT parameters for patients with immediately life-threatening, end-stage brain tumors remain unclear. We performed BNCT on 34 patients with life-threatening, end-stage brain tumors and analyzed the relationship between survival outcomes and BNCT parameters. Before BNCT, MRI and ¹⁸F-BPA-PET analyses were conducted to identify the tumor location/distribution and the tumor-to-normal tissue uptake ratio (T/N ratio) of ¹⁸F-BPA. No severe adverse events were observed (grade ≥ 3). The objective response rate and disease control rate were 50.0% and 85.3%, respectively. The mean overall survival (OS), cancer-specific survival (CSS), and relapse-free survival (RFS) times were 7.25, 7.80, and 4.18 months, respectively. Remarkably, the mean OS, CSS, and RFS of patients who achieved a complete response were 17.66, 22.5, and 7.50 months, respectively. Kaplan–Meier analysis identified the optimal BNCT parameters and tumor characteristics of these patients, including a T/N ratio ≥ 4, tumor volume < 20 mL, mean tumor dose ≥ 25 Gy-E, MIB-1 ≤ 40, and a lower recursive partitioning analysis (RPA) class. In conclusion, for malignant brain tumor patients who have exhausted all available treatment options and who are in an immediately life-threatening condition, BNCT may be considered as a therapeutic approach to prolong survival.

Despite standard treatment, about 70% of ovarian cancer will recur. Cancer stem cells (CSCs) have been implicated in the drug-resistance mechanism. Several drug resistance mechanisms have been proposed, and among these, autophagy plays a crucial role for the maintenance and tumorigenicity of CSCs. Compared to their differentiated counterparts, CSCs have been demonstrated to display a significantly higher level of autophagy flux. Moreover, mitophagy, a specific type of autophagy that selectively degrades excessive or damaged mitochondria, is shown to contribute to cancer progression and recurrence in several types of tumors. Nanomedicine has been shown to tackle the CSCs problem by overcoming drug resistance. In this work, we developed a nanomedicine, ¹⁸⁸Re-liposome, which was demonstrated to target autophagy and mitophagy in the tumor microenvironment. Of note, the inhibition of autophagy and mitophagy could lead to significant tumor inhibition in two xenograft animal models. Lastly, we presented two cases of recurrent ovarian cancer, both in drug resistance status that received a level I dose from a phase I clinical trial. Both cases developing drug resistance showed drug sensitivity to ¹⁸⁸Re-liposome. These results suggest that inhibition of autophagy and mitophagy by a nanomedicine may be a novel strategy to overcome drug resistance in ovarian cancer.

Background: This trial investigated the efficacy and safety of salvage boron neutron capture therapy (BNCT) combined with image-guided intensity-modulated radiotherapy (IG-IMRT) for recurrent head and neck cancer after prior radiotherapy (RT). Methods: BNCT was administered using an intravenous boronophenylalanine–fructose complex (500 mg/kg) in a single fraction; multifractionated IG-IMRT was administered 28 days after BNCT. For BNCT, the mucosa served as the dose-limiting organ. For IG-IMRT, the clinical target volume (CTV) and the planning target volume (PTV) were generated according to the post-BNCT gross tumor volume (GTV) with chosen margins. Results: This trial enrolled 14 patients, and 12 patients received combined treatment. The median BNCT average dose for the GTV was 21.6 Gy-Eq, and the median IG-IMRT dose for the PTV was 46.8 Gy/26 fractions. After a median (range) follow-up period of 11.8 (3.6 to 53.2) months, five patients had a complete response and four had a partial response. One patient had grade 4 laryngeal edema; another patient had a grade 4 hemorrhage. Most tumor progression occurred within or adjacent to the CTV. The 1-year overall survival and local progression-free survival rates were 56% and 21%, respectively. Conclusion: Despite the high response rate (64%) of this trial, there was a high incidence of in-field and marginal failure with this approach. Future studies combining BNCT with modalities other than radiation may be tried.

In esophageal cancer, few prediction tools can be confidently used in current clinical practice. We developed a deep convolutional neural network (CNN) with 798 positron emission tomography (PET) scans of esophageal squamous cell carcinoma and 309 PET scans of stage I lung cancer. In the first stage, we pretrained a 3D-CNN with all PET scans for a task to classify the scans into esophageal cancer or lung cancer. Overall, 548 of 798 PET scans of esophageal cancer patients were included in the second stage with an aim to classify patients who expired within or survived more than one year after diagnosis. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. In the pretrain model, the deep CNN attained an AUC of 0.738 in identifying patients who expired within one year after diagnosis. In the survival analysis, patients who were predicted to be expired but were alive at one year after diagnosis had a 5-year survival rate of 32.6%, which was significantly worse than the 5-year survival rate of the patients who were predicted to survive and were alive at one year after diagnosis (50.5%, p < 0.001). These results suggest that the prediction model could identify tumors with more aggressive behavior. In the multivariable analysis, the prediction result remained an independent prognostic factor (hazard ratio: 2.830; 95% confidence interval: 2.252–3.555, p < 0.001). We conclude that a 3D-CNN can be trained with PET image datasets to predict esophageal cancer outcome with acceptable accuracy.

Boron-10-containing positron emission tomography (PET) radio-tracer, ¹⁸F-FBPA, has been used to evaluate the feasibility and treatment outcomes of Boron neutron capture therapy (BNCT). The clinical use of PET/MR is increasing and reveals its benefit in certain applications. However, the PET/CT is still the most widely used modality for daily PET practice due to its high quantitative accuracy and relatively low cost. Considering the different attenuation correction maps between PET/CT and PET/MR, comparison of derived image features from these two modalities is critical to identify quantitative imaging biomarkers for diagnosis and prognosis. This study aimed to investigate the comparability of image features extracted from ¹⁸F-FBPA PET/CT and PET/MR. A total of 15 patients with malignant brain tumor who underwent ¹⁸F-FBPA examinations using both PET/CT and PET/MR on the same day were retrospectively analyzed. Overall, four conventional imaging characteristics and 449 radiomic features were calculated from PET/CT and PET/MR, respectively. A linear regression model and intraclass correlation coefficient (ICC) were estimated to evaluate the comparability of derived features between two modalities. Features were classified into strong, moderate, and weak comparability based on coefficient of determination (r²) and ICC. All of the conventional features, 81.2% of histogram, 37.5% of geometry, 51.5% of texture, and 25% of wavelet-based features, showed strong comparability between PET/CT and PET/MR. With regard to the wavelet filtering, radiomic features without filtering (61.2%) or with low-pass filtering (59.2%) along three axes produced strong comparability between the two modalities. However, only 8.2% of the features with high-pass filtering showed strong comparability. The linear regression models were provided for the features with strong and moderate consensus to interchange the quantitative features between the PET/CT and the PET/MR. All of the conventional and 71% of the radiomic (mostly histogram and texture) features were sufficiently stable and could be interchanged between ¹⁸F-FBPA PET with different hybrid modalities using the proposed equations. Our findings suggested that the image features high interchangeability may facilitate future studies in comparing PET/CT and PET/MR.