Technology in Cancer Research & Treatment最新文献

Papillary thyroid cancer (PTC), the most prevalent form of thyroid malignancy, is generally indolent but poses a recurrence risk of 10%-15%, leading to a clinical paradox: the need to mitigate recurrence while avoiding overtreatment. Current prognostic frameworks, reliant on anatomical and histopathological factors, often result in inefficient treatment pathways, unnecessary surgical interventions, and increased patient burden. The advent of molecular diagnostics presents a paradigm shift in risk stratification. Implementing preoperative molecular tests could transform PTC management by enabling tailored therapeutic strategies, reducing the need for completion thyroidectomies, optimizing the selection of patients for active surveillance, and refining the use of adjuvant therapies such as radioactive iodine. While genomic alterations such as BRAF and TERT mutations have been explored as prognostic markers, their predictive utility remains limited. In contrast, transcriptomic profiling has emerged as a powerful tool for identifying aggressive PTC subtypes with greater precision. Transcriptomic-based prognostic tests, like the novel Thyroid GuidePx^® classifier, effectively stratify PTCs into distinct molecular subgroups with differing recurrence risks, surpassing traditional clinicopathological models in predictive accuracy. By shifting toward biologically informed decision-making, we can enhance clinical efficiency, minimize patient morbidity, and improve overall healthcare resource utilization.

ObjectiveThis study analyzed the dosimetric impact of residual errors (rotational and deformation errors) in left-sided breast cancer radiotherapy after cone-beam CT (CBCT)-based translational errors correction.MethodsTwenty patients treated with intensity-modulated radiotherapy (IMRT) were retrospectively analyzed. Virtual CT images were generated by deforming and registering CBCT images with planning CT images. The accumulated dose was calculated to assess residual errors effects on target and organs at risk (OARs). A phantom test was conducted to evaluate rotational errors impacts.ResultsResults showed significant dose differences: for 4005 cGy, D98 and D95 of the breast (PTV_b) decreased, and mean dose, V30, and V20 of the left lung reduced; for 5000 cGy, D98 of the supraclavicular lymph nodes (PTV_sc) and PTVb, D95 of PTV_b, and mean dose and V20 of the heart differed significantly. Phantom simulations revealed that pitch angles ≤-1.8° and roll/yaw angles >2° caused overdosing in the left lung and heart, with maximum dose differences of 31.89% (heart) and 19.19% (lung) for 4005 cGy, and 26.32% (heart) and 20.92% (PTV_sc) for 5000 cGy.ConclusionResidual errors significantly affect dose distribution despite CBCT-based translational correction. Improved immobilization techniques or 6DOF couch correction are recommended to mitigate rotational errors.

IntroductionThis study sought to develop a predictive model using CT-based habitat radiomics to forecast pathological complete response (pCR) and progression-free survival (PFS) in esophageal squamous cell carcinoma (ESCC) patients receiving standardized neoadjuvant chemoradiotherapy (nCRT) followed by curative surgery.MethodsWe retrospectively analyzed baseline CT imaging data from 228 ESCC patients in a prospective cohort database. Patients were randomly divided into training and validation sets (7:3 ratio). Whole-tumor and habitat-derived radiomic features were extracted from pretreatment CT scans. For pCR prediction, habitat signatures were developed using Logistic Regression (LR), RandomForest (RF), and XGBoost models, optimized via grid search. PFS prediction employed Cox proportional hazards modeling with selected features. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow calibration curves, and decision curve analysis.ResultsThe habitat models retained 10 features for pCR prediction and 12 for PFS prediction. For pCR, the habitat-derived RF model demonstrated superior performance (training AUC: 0.821; validation AUC: 0.826), outperforming both other habitat models and the whole-tumor radiomics model (training AUC: 0.645). Similarly, the habitat-based RF model for PFS achieved higher AUCs (training: 0.759, 95% CI: 0.627-0.889; validation: 0.810, 95% CI: 0.653-0.966) compared to whole-tumor radiomics (training: 0.623; validation: 0.519).ConclusionOur analyses indicated a trend where habitat radiomics might outperform whole-tumor radiomics in predicting pCR and PFS for resectable ESCC after nCRT. While this merits further investigation, current evidence is insufficient to confirm its clinical utility for personalized treatment guidance.

ObjectivesIrreversible Electroporation (IRE) is both open surgery and minimally invasive cancer therapy used in the treatment of liver tumors. The therapy demands precision and accuracy to ensure complete tumor ablation. Reliable simulation tools can help achieve this goal by predicting the tissue regions that will reach the required electric field threshold and by suggesting correcting actions when the predicted outcome is inadequate. This article retrospectively compares segmented ablations from intra-procedural computed tomography (CT) scans with computer simulations to check their validity in predicting the operation outcome and the required electric field threshold.Methods10 patient ablation procedures were retrospectively analyzed using a detailed computational model of electroporation, informed by the patient-specific geometry of each case. CT scans were analyzed by three physicians over two sessions to assess intra- and inter-observer variability. Same day postoperative images were used for accuracy. The resulting measured ablations from the patient's data were compared to simulation predictions, both in terms of ablated volumes and 3D similarity scores (Dice coefficient).ResultsSimulated ablation volumes were computed across electric field thresholds (465-750 V/cm), showing highest volumes at 465 V/cm and lowest at 750 V/cm. Comparison with physician segmented volumes showed best match for 500-600 V/cm ablation threshold: this result was consistent across different patients despite differences among patient's conditions and characteristics. 3D analysis revealed Dice scores between 0.63 and 0.77 (mean: 0.71), indicating moderate to good agreement. Visual and statistical comparisons further validated the reliability of the simulation model within this threshold range.ConclusionThis study highlighted the accuracy of IRE ablation volume predictions by comparing retrospective CT based ablation volume segmentations with electric field simulations. The best match occurred at 500 to 600 V/cm thresholds, with post-procedure measurements. Despite observer variability and modeling limitations, Dice scores showed moderate to good agreement, validating the simulation model and emphasizing timely imaging for accuracy.

IntroductionSarcomatoid renal cell carcinoma (sRCC) is rare but highly aggressive and is associated with poor prognosis and limited treatment responsiveness. Despite several studies investigating its clinicopathological features, existing research is often limited by small sample sizes and short follow-up periods, and currently, no prognostic risk model is specific to patients with non-metastatic sRCC. This study aimed to investigate the clinicopathological characteristics of patients with non-metastatic sRCC and develop a predictive model for postoperative mortality risk.MethodsIn this retrospective study, we analyzed the clinical data of 45 patients diagnosed with non-metastatic sRCC who underwent surgical treatment at our institution's Department of Urology, between January 2008 and June 2024. These patients were compared with 527 patients with non-sarcomatoid renal cell carcinoma (non-sRCC). The primary endpoint was death, and the exact cause of death was recorded. Routine postoperative examinations and treatment details were documented through outpatient and inpatient electronic medical record systems.ResultsThe results indicated significant differences in body mass index, hypertension, surgical approach, nephrectomy type, surgical duration, maximum tumor diameter, tumor necrosis, T stage, and Ki-67 expression between patients with sRCC and those with non-sRCC (P < 0.05). Survival analysis revealed that the cancer-specific survival (CSS) for patients with sRCC was significantly lower than that for patients with non-sRCC (P < 0.001). Cox univariate and multivariate analyses identified maximum pathological tumor diameter, T stage, and high Ki-67 expression as independent risk factors. Based on these factors, we developed a postoperative mortality risk prediction model for patients with sRCC, with the calibration curves demonstrating a good fit of the model.ConclusionsThe proposed model is designed for patients with non-metastatic sRCC. It has potential clinical application value, aiding in the identification of high-risk patients and providing guidance for individualized treatment and close follow-up.

Lymphoma is a highly heterogeneous malignancy, demanding accurate and precise diagnosis to guide the selection of the appropriate treatment for optimal outcome. Copy number aberration (CNA) has been suggested to play an important role in the occurrence and development of lymphoma and thus can be explored as biomarker to improve disease management. It is believed that CNAs in variable forms and complexities can be triggered by both exogenous (eg viral infection and ionizing radiation) and endogenous factors (eg genetic predisposition and evolutionary forces). However, conventional cytogenetic methods have limitations to detect all types of CNAs with accuracy and adequate details. The emergence of new technologies, including fluorescence in situ hybridization (FISH), chromosome microarray analysis (CMA), and especially next-generation sequencing (NGS) has made significant progress in the identification and characterization of CNAs or CNA-related genomic aberrations. Accumulating data addressing molecular insights and clinical implications have provided us more theoretical and experimental support for its clinical translation. Currently, while only limited number of CNAs or CNA-related genomic variation, such as deletion/amplification of DNA segments, have been documented in major guidelines or consensus for their clinical potential in lymphoma, more CNAs remain to be further characterized and/or discovered for their clinical relevance. Taking together, with available and upcoming evidence, CNA should play an important role as a diagnostic and prognostic biomarker while integrated with the current settings in lymphoma.

IntroductionLung cancer has the highest mortality rate among all cancer types globally, largely due to delayed or ineffective diagnosis and treatment. Radiomics is commonly used to diagnose lung cancer, especially in later stages or during routine screenings. However, frequent radiological imaging poses health risks, and while advanced diagnostic alternatives exist, they are often costly and accessible only to a limited, privileged population. Leveraging clinical data using machine learning (ML) and artificial intelligence (AI) enables a safer, more inclusive, and affordable solution. Due to a lack of interpretability, AI-based models for cancer diagnosis are less adopted by clinicians.MethodsThis study introduces a safe, inclusive, and cost-effective lung cancer diagnostic method using an explainable AI (XAI) model built on routine clinical data. It employs a stacking ensemble of Artificial Neural Network (ANN) and Deep Neural Network (DNN) to match the diagnostic performance of clean-data DNN models. By incorporating rare medical cases through Adaptive Synthetic Sampling (ADASYN), the model reduces the risk of missing challenging, rare-case diagnoses.ResultsThe proposed XAI model demonstrates strong performance with an accuracy of 0.8558, AUC of 0.8600, precision of 0.8092, recall of 0.9282, and F1-score of 0.8646, notably improving rare case detection by over 50%. SHapley additive exPlanations(SHAP)-based interpretability highlights Erythrocyte sedimentation rate(ESR), intoxication-related factors, hemoglobin levels, and neutrophil counts as key features. The model also reveals associations, such as a link between heavy tobacco use and elevated ESR. Counterfactual explanations help identify features contributing to misdiagnoses by exposing sources of confusion in the model's decisions.ConclusionGiven the limited dataset size and geographic constraints, this research should be viewed as a prototype and in its current form, the model is best suited as a pre-screening tool to support early detection. With training on larger and more diverse datasets, the model has strong potential to evolve into a robust and scalable diagnostic solution.

Development of the Bharat Cancer Genome Atlas (BCGA) is poised to be a comprehensive genomic database which will not only deepen our scientific understanding of the unique molecular landscape of cancers prevalent in India but also provide the essential foundation required to facilitate the development of targeted therapies, enable personalized treatment strategies, and foster the creation of more effective early detection methods specifically tailored for the Indian population. The open-access nature of the BCGA is a core strength, designed to democratize access to this vital information, thereby empowering researchers to make new discoveries, enabling clinicians to provide more precise care, and allowing patients and their families to engage more fully in their health journey.

Early detection of skin cancer is crucial for effective treatment and improved patient outcomes. Recent advancements in oncologic imaging, particularly molecular imaging techniques, have revolutionized cancer diagnostics and treatment by enabling the visualization of tumors and cellular activities at the molecular level. These techniques facilitate the identification of early-stage cancers that might remain undetectable through traditional imaging methods. Innovative technologies such as reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) which visualize skin at near-histologic detail and skin fluorescent imaging (SFI), which targets αvβ3 integrin expression, are promising for non-invasive early detection of melanoma. By integrating in vivo molecular imaging with tumor biomarkers, clinicians can gain more precise insights into processes integral to cancer biology, leading to improved diagnosis, prognosis and the development of personalized treatment strategies. This review explores imaging modalities used in skin cancer diagnosis, highlighting their advantages and limitations, with an emphasis on molecular imaging, stressing its potential to improve early detection, personalize treatment and monitor therapeutic responses.

IntroductionAccurate beam modeling is essential for ensuring safe and effective proton therapy delivery. Before clinical implementation, pencil beam scanning systems require thorough validation to confirm that calculated dose distributions reliably reflect measured performance. This work outlines a practical approach to achieving comprehensive and efficient validation.MethodsThe beam model for a pencil beam scanning system was configured in the treatment planning system (TPS). Beam data including integrated depth dose, lateral profiles in air, and absolute outputs for various energies were measured and entered into the TPS following vendor recommendations. Validation tests were performed according to AAPM TG 185 and insights from other proton centers, adapted to our clinical requirements, time constraints, and regulations. The validation incorporated test cases from AAPM TG 350 draft report and included: 1) rectangular field dose distributions in water, 2) PDD measurements, 3) planar dose measurements using the DigiPhant detector with TG 350 test plans and clinical cases, and 4) end-to-end tests in animal tissue. TPS-calculated dose distributions, obtained using either the proton convolution superposition or Acuros Protons algorithms, were compared with corresponding measurements. A peer review from an institute with a similar proton treatment machine validated the machine output and our validation process.ResultsFor rectangular targets with various ranges and modulation widths in water based on TG 185, TG 350 test plans, and clinical plans, ionization chamber and MatriXX PT planar dose measurements agreed with TPS calculations (point dose difference < 3%, planar dose 3%/3 mm > 95%). Range differences for animal tissues were within 3%. Independent peer output measurements agreed with our results within 1%.ConclusionTPS-calculated range and dose were in good agreement with measurements across multiple validation tests. The beam model for both PCS and Acuros PT has been validated and used clinically. Incorporating practical considerations is essential for achieving comprehensive and efficient beam commissioning and validation.