Technology in Cancer Research & Treatment最新文献

IntroductionBreast cancer remains a leading cause of cancer mortality despite being potentially curable when detected early, particularly in low- and middle-income countries where access to screening is limited. This is largely driven by operational gaps, including limited access to screening and delays in diagnosis and treatment. JULIETA is a portable bioimpedance spectroscopy device designed to identify electrical tissue patterns associated with potentially malignant findings and to prioritize women for further diagnostic evaluation. This study assessed the performance of a hierarchical algorithm integrated into JULIETA to distinguish findings without malignant potential (BI-RADS 1-2) from those with malignant potential (BI-RADS ≥3), using mammography as the reference standard.MethodsA cross-sectional observational study with prospective data collection was conducted between May and July 2024 in four Colombian cities. Adult women undergoing screening or follow-up mammography were evaluated with JULIETA prior to imaging. Impedance-derived features, breast density estimates, and individual risk scores were used to retrain a hierarchical classifier combining Random Forest and SVM-RBF models, using an 80/20 stratified split and cross-validation.ResultsA total of 1350 women were recruited (mean age 56.5 ± 8.0 years); 67% were BI-RADS 1-2 and 21% BI-RADS 4. After data cleaning, 673 breasts (469 women) were included. The model achieved 73% sensitivity, 76% specificity, 65.5% positive predictive value, and 82.1% negative predictive value.ConclusionJULIETA is a feasible, safe, and reproducible noninvasive bioimpedance pre-screening tool that may enable scalable triage and support earlier detection and improved equity when integrated into public health pathways.

IntroductionPreoperative differentiation among adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) is crucial for guiding ground-glass nodule (GGN) management. This multicenter study evaluated the comparative utility of deep learning (DL), radiomics, and conventional machine learning (cML)-based clinicoradiographic models for this ternary classification.MethodsWe developed four DL models (DenseNet-121, ResNet-10, ResNet-18, and VGG-13) for the ternary classification of AIS, MIA, and IAC using multicenter CT datasets. For comparative analysis, we constructed two additional classification models: (1) a radiomics model employing feature engineering through analysis of variance, recursive feature elimination with cross-validation, and least absolute shrinkage and selection operator, and (2) the cML-based clinicoradiographic model utilizing 12 different classifiers. The performance of all models was evaluated using the macro area under the curve (Macro-AUC) metric.Results847 GGNs postoperatively confirmed as lung adenocarcinoma were included in this multicenter study, which were randomly split into a training set (70%, n=592) and a validation set (30%, n=255). The DL model ResNet-10 demonstrated superior performance, achieving a Macro-AUC of 0.8055 (95% CI: 0.7723-0.8387), an accuracy of 0.6300 (95% CI: 0.5541-0.6764), and an F1-score of 0.4206 (95% CI: 0.3821-0.4598). This performance surpassed that of the radiomics model, which had a Macro-AUC of 0.7801 (95% CI: 0.7432-0.8170), an accuracy of 0.6100 (95% CI: 0.5276-0.6204), and an F1-score of 0.5505 (95% CI: 0.4983-0.6017), and the cML-based clinicoradiographic model, which achieved a Macro-AUC of 0.7770 (95% CI: 0.708-0.846), an accuracy of 0.6000 (95% CI: 0.5376-0.6604), and an F1-score of 0.4438 (95% CI: 0.3925-0.4961).ConclusionThe ResNet-10 network established a novel ternary classification model for predicting the invasiveness of GGNs. This approach provides clinically actionable insights that support surgical planning and facilitate risk-adapted management.

BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin-lymphoma. Although it can be cured in many patients, a significant proportion of patients fail the primary treatment and require second-line treatment. Currently, only limited data on real-world outcomes with standard therapies in Austrian patients with DLBCL are available, and while novel therapies are emerging, no historical benchmarks have been established to serve as a reference for these novel treatments.MethodsWe performed a retrospective, single-center analysis of patients with DLBCL diagnosed between 2010 and 2018 who had been treated with standard therapies. To establish efficacy benchmarks for novel therapies, we applied both clinical-trial and real-world-derived criteria to analyze the outcomes of patients potentially eligible for novel or future treatments.ResultsAlthough many patients can be cured with frontline therapy, outcomes are poor, especially in high-risk patients. Patients failing frontline therapy, especially those fulfilling the chimeric antigen-receptor (CAR) T-cell eligibility criteria, had dismal outcomes, and very few patients achieved long-term remission. Our data provide benchmark outcomes for patients eligible for novel treatments such as antibody-drug-conjugate (ADC) or CAR T-cell therapy-based treatments for potential future comparative analyses.ConclusionsPatients with DLBCL treated in Austria showed comparable outcomes to those reported in other real-world studies. Overall, standard chemotherapy-based approaches provide unsatisfactory outcomes in high-risk patients and patients in whom frontline therapy fails. Because many patients are now eligible for alternative first- and second-line treatments, such as ADC-based or CAR T-cell therapy, our efficacy benchmarks can serve for the future evaluation of these therapies in the Austrian healthcare environment.

Ovarian cancer (OC), one of the most lethal gynecological malignancies, urgently requires breakthrough diagnostic and therapeutic strategies due to its low survival rate and high recurrence rate. The gut microbiota (GM), which colonizes the human gastrointestinal tract, significantly influences human health. Recent technological advancements have enabled deeper investigation into tumor-bacteria interactions. The GM profoundly participates in OC initiation, progression, and treatment resistance by dynamically regulating the host's immune response, metabolism, and inflammatory microenvironment. This review focuses on three primary mechanisms by which the GM influences OC development and its impact on cancer therapies (chemotherapy, immunotherapy, and targeted therapy). At the mechanistic level, GM dysbiosis promotes OC through multiple pathways: (1) Modulating the tumor microenvironment (TME), including inducing immunosuppressive cell infiltration and impairing anti-tumor immunity; (2) Interfering with estrogen metabolism, thereby elevating bioactive estrogen levels; (3) Producing metabolites that mediate systemic inflammatory signaling and energy metabolism reprogramming. These alterations collectively drive tumor proliferation and metastasis. Although microbiota-based interventions offer novel opportunities for precision therapy in OC, clinical translation faces challenges such as mechanistic complexity and individual heterogeneity. Future research should integrate multi-omics technologies and large-scale clinical trials to advance microbiota modulation strategies from bench to bedside, thereby improving OC prognosis.

With increasing limitations on reproductive choice in the past several years, reproductive rights-often relegated to abortion access and contraception-have become a critical consideration for American clinicians and patients. We implore the medical community to expand its understanding of reproductive autonomy by illuminating an overlooked community: those with hereditary breast and ovarian cancer syndrome. For those with pathogenic/likely pathogenic variants in cancer susceptibility genes that carry a 50% inheritance pattern, such as BRCA, preimplantation genetic testing for monogenic disorders offers a life-altering technology that provides the option to halt the generational legacy of cancer. This service, however, is cost-prohibitive. These financial barriers create disparities that not only directly impact the immediate offspring but also have the potential to form entire generational shifts in future gene pools based on socioeconomic status. This form of healthcare injustice is unacceptable, for which the medical community can and should advocate for urgent solutions. We posit that genomic justice, specifically within the framework of reproductive justice, demands that all communities have the ability to choose whether and how they will use genomic technologies to align with their reproductive goals and values.

Primary bone malignancies - including chordoma, chondrosarcoma, osteosarcoma, and Ewing sarcoma - originate from bone or cartilage cells and often develop in anatomically complex or surgically challenging regions. While surgical resection remains the standard of care for most localized tumors, radiation therapy (RT) has become an increasingly integral component of multidisciplinary management, particularly when complete surgical excision is not feasible, margins are close or positive, or the tumor is adjacent to critical structures. Historically, conventional photon-based RT has shown limited efficacy in many of these tumors due to factors such as relative radioresistance and proximity to radiosensitive normal tissues. However, advances in conformal photon techniques such as intensity-modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT), along with hadron-based approaches like proton beam therapy (PBT) and carbon ion radiation therapy (CIRT), have expanded the therapeutic potential of RT in bone sarcomas. This review highlights the evolving role of RT in the management of primary bone malignancies, with a focus on technological advances, clinical outcomes, ongoing trials, and future directions in the field.

IntroductionImmune checkpoint inhibitors (ICIs) are extensively utilized in lung cancer patients, with documented instances of ICIs-associated acute kidney injury (ICIs-AKI). This study aims to explore a model for early recognition of ICIs-AKI.MethodsThe study involved 413 adult lung cancer patients treated with ICIs at Ningbo No.2 Hospital between Sept. 1, 2021, and June 30, 2023. Patients were followed until death or Dec. 31, 2023, and categorized into ICIs-AKI or non-AKI groups. We employed univariate and multivariate logistic regression to identify risk factors, developed both logistic regression and Multilayer Perceptron (MLP) prediction models, and used Kaplan-Meier survival analysis to assess prognosis.ResultsThe study included 381 lung cancer patients receiving ICIs treatment after excluding 32 patients. ICIs-AKI occurred in 13.39% of cases, with a median onset time of [123 (63, 303)] days. Multivariable logistic analysis identified diabetes, proteinuria, extrarenal irAEs, diuretic use, and chemotherapy as significant risk factors (all P < 0.05), while higher baseline eGFR levels were protective (P < 0.05). Two prediction models were developed: logistic regression (AUC=0.877, sensitivity=0.922, specificity=0.726) and MLP (AUC=0.950, accuracy=0.843, precision=0.847). Survival analysis showed no difference in overall survival between ICIs-AKI and non-AKI groups (HR = 1.021, 95% CI = 0.629-1.659, P = 0.932; adjusted HR = 0.950, 95% CI = 0.558-1.616, P = 0.849). AKI to CKD progression incidence was 58.82%, with no significant difference in overall survival between CKD and non-CKD groups (P = 0.157).ConclusionThis study offers detailed insights into ICIs-AKI, including its rate, onset timing, risk factors, and clinical features. Approximately half of the affected patients experienced spontaneous renal function recovery. Both logistic regression and MLP models effectively predicted ICIs-AKI. Importantly, neither ICIs-AKI incidence nor renal function restoration correlated with patient mortality. These findings underscore the importance of early detection and management strategies.

IntroductionDaily anatomical variations in prostate cancer radiotherapy, particularly due to pelvic organ motion and filling, can compromise target coverage and increase exposure to organs at risk (OARs). Conventional image-guided radiotherapy (IGRT) uses fixed safety margins and daily couch corrections to account for these variations, potentially leading to overtreatment of healthy tissue or insufficient tumor coverage. Online adaptive radiotherapy (oART), based on cone-beam computed tomography (CBCT), enables daily plan adaptation to the patient's anatomy, offering improved precision, enhanced target coverage, and better OAR sparing. This retrospective study compares oART to conventional IGRT in prostate cancer treatment.MethodsA total of 153 treatment fractions from six consecutive prostate cancer patients treated with oART on a Varian Ethos system were analyzed. For each fraction, three plans were evaluated: the scheduled plan (initial plan recalculated on daily CBCT), the adapted plan (reoptimized based on daily anatomy), and the verification plan (applied dose recalculated on a post-adaptation CBCT). Dose-volume metrics for target volumes and OARs were assessed, and clinical acceptability was evaluated. Interfractional prostate volume changes and treatment times were examined.ResultsCTV D_98% improved significantly with adaptation (median 97.85% to 98.55%; p < 0.01) and further increased in the verification plan (98.8%; p < 0.01), alongside reduced interquartile ranges. PTV D_98% rose from 90.1% to 97.1% with adaptation and to 96.9% after verification (p < 0.01). Bowel and bladder doses showed dosimetrical advantage. Clinically acceptable plans increased from 24.8% (scheduled) to 98% (adapted) and 85.6% (verification). Scheduled plans were not used clinically. Median prostate volume remained stable despite inter-individual variation. oART required about twice the treatment time of IGRT.ConclusionAlthough more time-consuming, oART improved target dose coverage and optimized OAR sparing, while simultaneously reducing dose variability for both the target and some OARs compared to IGRT. The plan acceptability improved significantly.