RESUMO
Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In this study, we constructed prediction models using handcrafted radiomics (HCR) and deep learning radiomics (DLR) based on pretreatment MRI data to predict CRT response in LACC. Furthermore, we investigated the potential improvement in prediction performance by incorporating clinical factors. A total of 252 LACC patients undergoing curative chemoradiotherapy are included. The patients are randomly divided into two independent groups for the training (167 patients) and test datasets (85 patients). Contrast-enhanced T1- and T2-weighted MR scans are obtained. For HCR analysis, 1890 imaging features are extracted and a support vector machine classifier with a five-fold cross-validation is trained on training dataset to predict CRT response and subsequently validated on test dataset. For DLR analysis, a 3-dimensional convolutional neural network was trained on training dataset and validated on test dataset. In conclusion, both HCR and DLR models could predict CRT responses in patients with LACC. The integration of clinical factors into radiomics prediction models tended to improve performance in HCR analysis. Our findings may contribute to the development of personalized treatment strategies for LACC patients.
Assuntos
Aprendizado Profundo , Neoplasias do Colo do Útero , Feminino , Humanos , Quimiorradioterapia/métodos , Imageamento por Ressonância Magnética/métodos , Radiômica , Estudos Retrospectivos , Neoplasias do Colo do Útero/diagnóstico por imagem , Neoplasias do Colo do Útero/terapiaRESUMO
The reasons for sex-associated gut microbiota differences have not been determined, and although sex hormones, diet, and other factors are considered to contribute to them, many of these factors are age related. To shed light on this complex interplay, our study aimed to investigate and compare the gut microbial compositions of males and females across a broad range of ages, aiming to identify sex-associated disparities and potential causal factors. Our study encompassed a comprehensive analysis of gut microbiota data obtained from 444 Japanese individuals, ranging from newborns to centenarians, sourced from the DNA Data Bank of Japan. We categorized the subjects into 13 distinct age groups and examined their relative microbial abundances, as well as alpha and beta diversities, in relation to sex and age. No difference was observed between gut microbiota relative abundances or alpha diversities between men and women at any age. However, the study showed that the heterogeneity of gut microbiota among women in their 20s was greater than in men. To confirm the general occurrence of this difference, we conducted additional analyses using seven datasets: three from Japan and four from other countries. Interestingly, this variance was particularly noticeable within Japanese women. We also showed a potential link between the observed heterogeneity and dietary fiber intake. It is hoped this study will provide clues that aid in the identification of factors responsible for sex-associated differences in gut microbiota compositions.
RESUMO
BACKGROUND/AIM: This study evaluated the possibility of clinical use of circulating-tumor DNA (ctDNA) as a biomarker to determine up-front autologous stem cell transplantation (auto-SCT) for patients with high-risk diffuse large B-cell lymphoma (DLBCL) in practice. PATIENTS AND METHODS: To explore the dynamics of ctDNA in DLBCL, blood samples were collected sequentially before and after treatment from patients with newly diagnosed DLBCL who received rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. To conduct ctDNA genotyping and ctDNA monitoring simultaneously, targeted sequencing by cancer personalized profiling using deep sequencing was used. RESULTS: Ten patients between the ages of 50 and 60 years were enrolled. Based on the international prognostic index (IPI), seven patients were classified as high-IPI-risk group, and three patients were classified as low-IPI-risk group. The IPI risk group correlated with total metabolic tumor volume. All patients completed six cycles of R-CHOP chemotherapy, and seven patients achieved complete response. Changes in ctDNA mutation numbers did not correlate with changes in PET scan images and treatment response. In most high-risk patients, new mutations appeared in ctDNA after completion of chemotherapy that conceivably marked resistant clones. Notably, disease relapse did not occur in high-risk patients with poor prognostic mutations who underwent autologous SCT. CONCLUSION: ctDNA monitoring was meaningful in high-risk patients. Moreover, ctDNA and well-known prognostic factors should be considered in the decision making for auto-SCT. If a new genetic mutation in ctDNA with a negative prognosis would emerge during treatment, high-risk patients should consider auto-SCT.
Assuntos
Transplante de Células-Tronco Hematopoéticas , Linfoma Difuso de Grandes Células B , Humanos , Pessoa de Meia-Idade , Projetos Piloto , Recidiva Local de Neoplasia/tratamento farmacológico , Transplante Autólogo , Transplante de Células-Tronco , Linfoma Difuso de Grandes Células B/diagnóstico , Linfoma Difuso de Grandes Células B/tratamento farmacológico , Linfoma Difuso de Grandes Células B/genética , Prognóstico , Ciclofosfamida/uso terapêutico , Prednisona/uso terapêutico , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Doxorrubicina/uso terapêutico , Doxorrubicina/efeitos adversos , Vincristina/uso terapêutico , DNARESUMO
BACKGROUND/AIM: To date, therapeutic options for T-cell acute lymphoblastic leukemia (T-ALL) remain very limited. This study evaluated the efficacy of monotherapies and combination therapies including a selective BCL-2 inhibitor for T-ALL cell lines, namely Jurkat, CCRF-CEM, and Loucy. MATERIALS AND METHODS: Loucy is an early T-precursor ALL (ETP-ALL) cell line characterized by an immature phenotype, whereas Jurkat and CCRF-CEM are late T-cell progenitor ALL (LTP-ALL) cell lines. Monotherapy was conducted with venetoclax, cytarabine, bendamustine, or azacytidine, whereas combination therapy was performed with venetoclax plus cytarabine, venetoclax plus bendamustine, or venetoclax plus azacytidine. Cell viability assay was conducted after 48 h using Trypan blue and the 3-(4, 5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS). Statistical analysis for evaluating synergistic interactions between anticancer drugs was performed by using the SynergyFinder Plus and drc R package. RESULTS: Adding venetoclax to cytarabine, bendamustine, or azacitidine achieved an additive effect, with Loewe synergic scores ranging from -10 to 10 in Jurkat and CCRF-CEM. Conversely, the combination of venetoclax and cytarabine displayed an additive effect (Loewe synergic score: 8.45 and 5.82 with MTS and Trypan blue assays, respectively), whereas venetoclax plus bendamustine or azacitidine exhibited a synergistic effect (Loewe synergic score >10 with MTS assay) in Loucy. Remarkably, the Bliss/Loewe score revealed that the combination of venetoclax and bendamustine was the most synergistic, yielding a score of 13.832±0.55. CONCLUSION: The combination of venetoclax and bendamustine demonstrated the greatest synergistic effect in suppressing ETP-ALL cell proliferation. Further studies are warranted to determine the mechanisms for the synergism between venetoclax and bendamustine in high-risk T-ALL.