Cancer Treatment Planning Research Articles

Oncofertility in primary health care: the role of the Portuguese family doctor

Abstract Background The increasing number of cancer cases diagnosed at young ages and the impact of the disease and treatments on reproductive function have led to the development of Oncofertility. While oncologists typically lead these discussions, family doctors, as primary care providers, also play a crucial role. This study aims to assess family doctors’ knowledge, attitudes, and practices regarding fertility preservation in cancer patients. Methods This cross-sectional study, conducted between April 2023 and March 2024, surveyed 351 General and Family Medicine doctors from the Portuguese Northern Regional Health Administration. The questionnaire, consisting of 15 questions, assessed sociodemographic profiles, knowledge, attitudes, and practices. Data were analyzed using descriptive statistics and multivariate logistic regression. Results Most participants were female (80.5%) and under 40 years old. The majority (91.5%) had treated fewer than 10 cancer patients under 40 in the past two years. Key barriers included lack of knowledge (63.3%), time constraints (57.0%), and patient age (18.5%). Male physicians were more likely to address specific fertility concerns (OR 2.239) and less likely to view patient age as a barrier (OR 0.459). Physicians aged 31-35 were less likely to discuss fertility preservation (OR 0.553) but more likely to address it in the first consultation (OR 2.969). Urban-based physicians were less likely to discuss fertility preservation (OR 2.099). Only 9.1% always discussed fertility preservation before treatment, 27.6% did so sometimes, and 63.3% never discussed it. Conclusions There are significant gaps in the knowledge and practices of family doctors regarding fertility preservation for cancer patients. Targeted training and resources are urgently needed to support family doctors in effectively discussing fertility preservation, crucial for improving the quality of life of young cancer patients wishing to preserve their fertility. Key messages • Empowering Portuguese family doctors to address fertility concerns in cancer patients contributes to comprehensive oncofertility care, improving patient outcomes and quality of life. • Integrating oncofertility support with Portuguese family doctors strengthens primary care, ensuring discussion of fertility preservation is considered as part of cancer treatment planning.

Empowering Wellness: A Comprehensive Narrative Review of Cancer Prehabilitation from Treatment Onset to Surveillance.

Cancer prehabilitation, defined as a process occurring between cancer diagnosis and the onset of acute treatment, is highlighted for its ability to enhance physical and mental health results while decreasing overall healthcare costs. This summary introduces the concept of cancer prehabilitation and emphasises the crucial role of oncology nurses in rehabilitation care. The cancer treatment plan of prehabilitation requires timely and efficient assessment across the care continuum, focusing on enhancing outcomes at every stage of cancer. The battle with cancer involves three different assessments with distinct goals: i) prehabilitation evaluation before treatment, ii) early post-treatment evaluations of rehabilitation and iii) final evaluations of health promotion. Analyses and treatments for significant side effects or complications associated with the treatment specifically for prehabilitation are recommended. The roles of coordination, counseling, preparing for discharge and teaching are outlined as integral components of a cancer nurse's responsibilities in the prevention of cancer. A literature search from March 2016 to June 2023 was conducted using the keywords 'neoplasms,' 'cancer,' 'prehabilitation,' 'continuum of care,' 'care continuum,' 'patient care continuity,' 'epidemiology,' 'therapeutics,' 'health,' 'prevention and control' and 'guidelines as topic.' The findings suggest that care coordinators or navigators for cancer should be educated to assess the physical and psychological status of patients once a cancer diagnosis is confirmed, particularly for those awaiting surgery at home. To enhance their competence in prehabilitation care, oncology nurses are encouraged to gain knowledge of certain tumours' outcomes and cancer-related treatments. Additionally, improving the ability to evaluate patients' functional status and emotional distress is crucial for oncology nurses involved in cancer prehabilitation.

The Role of Contrast-Enhanced Imaging Methods in Breast Cancer Early Diagnostics and Treatment Planning

Оn March 27, 2023, а multidisciplinary Advisory Board meeting was held in Moscow with the participation of Е.V. Artamonova, Е.А. Busko, V.V. Didenko, А.D. Zikiryakhodzhaev, М.S. Karpova, G.Е. Kvetenadze, P.V. Krivorot’ko, М.L. Mazo, А.V. Petrovsky, О.S. Puchkova, S.V. Serebryakova under the chairmanship of I.Е. Tyurin and N.I. Rozhkova. The participants discussed different issues of early diagnostics and treatment planning of breast cancer. We present the conclusion made by the Advisory Board.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Improvement of treatment plan quality with modified fixed field volumetric modulated arc therapy in cervical cancer.

This study aims to introduce modified fixed field volumetric modulated arc therapy (MF-VMAT) which manually opened the field size by fixing the jaws and comparing it to the typical planning technique, auto field volumetric modulated arc therapy (AF-VMAT) in cervical cancer treatment planning. Previously treated twenty-eight cervical cancer plans were retrospectively randomly selected and replanned in this study using two different planning techniques: AF-VMAT and MF-VMAT, resulting in a total of fifty-six treatment plans. In this study, we compared both planning techniques in three parts: (1) Organ at Risk (OARs) and whole-body dose, (2) Treatment plan efficiency, and (3) Treatment plan accuracy. For OARs dose, bowel bag (p-value=0.001), rectum (p-value=0.002), and left femoral head (p-value=0.001) and whole-body (p-value=0.000) received a statistically significant dose reduction when using the MF-VMAT plan. Regarding plan efficiency, MF-VMAT exhibited a statistically significant increase in both number of monitor units (MUs) and control points (p-values=0.000), while beam-on time, maximum leaf travel, average maximum leaf travel, and maximum leaf travel per gantry rotation were statistically significant decreased (p-values=0.000). In terms of plan accuracy, the average gamma passing rate was higher in the MF-VMAT plan for both absolute dose (AD) (p-value=0.001, 0.004) and relative dose (RD) (p-value=0.000, 0.000) for 3%/3 and 3%/2mm gamma criteria, respectively. The MF-VMAT planning technique significantly reduces OAR doses and decreases the spread of low doses to normal tissues in cervical cancer patients. Additionally, this planning approach demonstrates efficient plans with lower beam-on time and reduced maximum leaf travel. Furthermore, it indicates higher plan accuracy through an increase in the average gamma passing rate compared to the AF-VMAT plan. Consequently, MF-VMAT offers an effective treatment planning technique for cervical cancer patients.

HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set

The search for cancer biomarkers is of great significance for the early diagnosis and clinical treatment planning of pancreatic cancer. RNA transcription networks can effectively represent the interactions between RNA molecules, and the irregular perturbations in these interactions can lead to pathological states, making them commonly used for identifying cancer biomarkers. However, traditional network-based methods for identifying cancer biomarkers often rely on prior information and mutation data. Furthermore, most network controllability-based methods face the limitation of high computational complexity, preventing their integration with deep learning methods. In this paper, we propose a novel deep learning framework, called HATZFS, for identifying driver biomarkers in pancreatic cancer using RNA differential expression data. The proposed model combines hierarchical deep Q-network (HDQN), graph attention network (GAT), and zero-forcing set (ZFS). Firstly, a pancreatic cancer RNA transcription regulatory network is constructed to embed the complex relationship among Long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA). Then, we transform it into multiple subgraphs using graph sampling method, and apply GAT to learn the node features of the subgraphs. Finally, the HATZFS performs HDQN to select subgraphs and identify important RNA as driver nodes, and determining the controllability of the subgraphs based on ZFS. More important, the paper theoretically proves that when all subgraphs are controllable, the original graph is also controllable, and the union of the driver node sets of all subgraphs is equal to the driver node set of the original graph. To confirm the effectiveness of the proposed model, comprehensive experiments are conducted on the benchmark dataset consisting of 14 databases, comparing it with eight other algorithms. The experimental results demonstrate that the proposed model outperforms other methods. Overall, HATZFS not only provides the importance of all RNA molecules in the pancreatic cancer RNA regulatory network, but also identifies the driver node set of the network as driver biomarkers. Source code can be accessed at https://github.com/HongJieTongXue/HATZFS.

LesionMix data enhancement and entropy minimization for semi-supervised lesion segmentation of lung cancer

Determining the location and contour of the lesion is a crucial prerequisite for medical diagnosis, subsequent personalized treatment plan and prognostic prediction of lung cancer. Semi-supervised learning and data augmentation methods facilitate deep learning to be used in many fields of medical imaging. In this paper, we introduce a novel data enhancement technique called LesionMix. This method involves extracting and reusing lesions from a limited amount of labeled CT data, thereby enhancing the efficiency of utilizing those labeled data. Meanwhile, we propose a two-stage semi-supervised training strategy called Entropy Minimization LesionMix (EMLM). In the first stage, features containing lesion contour information are rapidly learned through LesionMix data augmentation. Entropy minimization strategy optimizes the model parameters to alleviate overfitting as much as possible in the second stage and improves prediction confidence. Our proposed method is validated on the public dataset LIDC-IDRI and the in-house dataset GDPHLUAD. Extensive experiments demonstrate that our method achieves promising performance and outperforms seven state-of-the-art semi-supervised models; Moreover, ablation experiments validate the effectiveness of various aspects of our approach.

Timing of breast biopsy and axillary ultrasound does not affect the false positive rate of the axillary ultrasound

BackgroundThe determination of axillary lymph node metastases is an important component of breast cancer treatment planning. Lymph node biopsies can be performed simultaneously with breast biopsy or at a later time. However, a post-breast biopsy ultrasound of the axilla may appear abnormal due to reactivity in the axillary lymph nodes. This study examined the timing of the axillary ultrasound (AUS) in relation to a breast biopsy to determine if it would affect patient management. MethodsAll newly diagnosed breast cancers from 2016 to 2017 were analyzed. We analyzed the timing of breast abnormality diagnosis, breast ultrasound, breast biopsy, AUS, and the AUS biopsy (AUSBx). An interval AUS was considered one that was not performed on the same day as a breast biopsy. True positive (TP), false positive (FP), true negative (TN), and false negative (FN) values were calculated. Chi-square analysis and Fisher exact test were performed on categorical data, and a student’s t-test was performed for continuous variables. Significance was determined to be p < 0.05. ResultsSame-day TP and FP AUS rates were 66.5 % and 33.5 %, respectively, and interval TP and FP AUS rates were 60.1 % and 39.9 %, respectively (p = 0.4708). The same-day TN and FN AUS rates were 90.6 % and 9.4 %, respectively, and interval TN and FN rates were 82.4 % and 17.6 %, respectively (p = 0.3917). ConclusionOverall TP, FP, TN, and FN rates did not differ between patients who received same-day or interval AUS studies.

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy.

The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

A novel GAN-based three-axis mutually supervised super-resolution reconstruction method for rectal cancer MR image

Background and objectiveThis study aims to enhance the resolution in the axial direction of rectal cancer magnetic resonance (MR) imaging scans to improve the accuracy of visual interpretation and quantitative analysis. MR imaging is a critical technique for the diagnosis and treatment planning of rectal cancer. However, obtaining high-resolution MR images is both time-consuming and costly. As a result, many hospitals store only a limited number of slices, often leading to low-resolution MR images, particularly in the axial plane. Given the importance of image resolution in accurate assessment, these low-resolution images frequently lack the necessary detail, posing substantial challenges for both human experts and computer-aided diagnostic systems. Image super-resolution (SR), a technique developed to enhance image resolution, was originally applied to natural images. Its success has since led to its application in various other tasks, especially in the reconstruction of low-resolution MR images. However, most existing SR methods fail to account for all anatomical planes during reconstruction, leading to unsatisfactory results when applied to rectal cancer MR images. MethodsIn this paper, we propose a GAN-based three-axis mutually supervised super-resolution reconstruction method tailored for low-resolution rectal cancer MR images. Our approach involves performing one-dimensional (1D) intra-slice SR reconstruction along the axial direction for both the sagittal and coronal planes, coupled with inter-slice SR reconstruction based on slice synthesis in the axial direction. To further enhance the accuracy of super-resolution reconstruction, we introduce a consistency supervision mechanism across the reconstruction results of different axes, promoting mutual learning between each axis. A key innovation of our method is the introduction of Depth-GAN for synthesize intermediate slices in the axial plane, incorporating depth information and leveraging Generative Adversarial Networks (GANs) for this purpose. Additionally, we enhance the accuracy of intermediate slice synthesis by employing a combination of supervised and unsupervised interactive learning techniques throughout the process. ResultsWe conducted extensive ablation studies and comparative analyses with existing methods to validate the effectiveness of our approach. On the test set from Shanxi Cancer Hospital, our method achieved a Peak Signal-to-Noise Ratio (PSNR) of 34.62 and a Structural Similarity Index (SSIM) of 96.34 %. These promising results demonstrate the superiority of our method.

Molecular Genetics MDT, a new experience at Warith International Cancer Institute/Iraq

Introduction: The advances in molecular targeted therapies made huge differences in cancer care. Genome analysis become more frequent during practice; genomic test results need to be discussed interdisciplinary to reach the maximum treatment benefit. Methods: We established the first multidisciplinary cancer genetics meeting at Warith international cancer institute to discuss patients with different malignancies from a molecular perspective. Results: From May 2024 to September 2024, 15 cases were discussed in the molecular genetics MDT. In regard to many clinical scenarios like: patient centered care, limitation of treatment toxicity, preserving quality of life, tailoring cancer treatment plan and advanced genetic counselling techniques. Conclusion: This approach tends to be successful in discussing cancer cases by the involvement of different specialties to reach optimal cancer care. Although, it highlighted the significance of investment in cancer genomic infrastructure. Hence, improving patient’s outcome and personalize cancer care.

Modulation of Multidrug Resistance Transporters by Food Components and Dietary Supplements: Implications for Cancer Therapy Efficacy and Safety.

The aim of this review is to explore how diet and dietary supplements influence the activity of key multidrug resistance (MDR) transporters-MRP2, BCRP, and P-gp. These transporters play a crucial role in drug efflux from cancer cells and significantly affect chemotherapy outcomes. This review focuses on how dietary phytochemicals, such as catechins and quercetin, impact the expression and function of these transporters. Both in vitro and in vivo experiments were examined to assess changes in drug bioavailability and intracellular drug accumulation. The findings show that certain dietary components-such as catechins, flavonoids, resveratrol, curcumin, terpenoids, sterols, and alkaloids-can either inhibit or induce MDR transporter activity, thus influencing the effectiveness of chemotherapy. These results highlight the importance of understanding diet-drug interactions in cancer therapy to improve treatment outcomes and reduce side effects. In conclusion, dietary modifications and supplements should be carefully considered in cancer treatment plans to optimize therapeutic efficacy.

End of life care for people with dementia and comorbid cancer: recent studies and research gaps.

This narrative review examines recent research on end of life (EOL) care for people with dementia and comorbid cancer, highlighting the complexity and challenge of providing effective EOL care for this group and areas of interest for future research. People with cancer and dementia and their family/supporters have more complex care and support needs at EOL that are less well met than those of older adults with cancer alone, including questionable use of aggressive EOL care interventions, poorer access to specialist palliative care teams and poor pain management. Those diagnosed with dementia at the same time as, or after their cancer diagnosis, are at greater risk of aggressive EOL care and EOL cancer treatment and care plans that are not able to meet their care needs as dementia progresses. There is a lack of research on EOL care for people with cancer and dementia. There is little understanding of what good care for this population looks like and if and how EOL care can best meet the needs of people with cancer and dementia or their carers/supporters. More research is needed to inform improved care for this population.

2403: Specific dose optimization driven by late toxicity subregions in prostate cancer treatment plans

2403: Specific dose optimization driven by late toxicity subregions in prostate cancer treatment plans

Feasibility of structuring electronic health record data to facilitate real-world data research: ICAREdata methods applied to multicenter cancer clinical trials.

The use of electronic health record (EHR) data for research is limited by a lack of structure and a standard data model. The objective of the ICAREdata (Integrating Clinical Trials and Real-World Endpoints Data) project was to structure key research data elements in EHRs using a minimal Common Oncology Data Elements (mCODE) data model to extract and transmit data. The ICAREdata project captured two EHR data elements essential to clinical trials: cancer disease status and treatment plan change. The project was implemented in clinical sites participating in Alliance for Clinical Trials in Oncology trials. Data were extracted from EHRs and sent by secure Fast Healthcare Interoperability Resource messaging (a standard for exchanging EHRs) to a database. Selected elements were compared with corresponding data from the trial's electronic data capture (EDC) system, Medidata Rave. By December 2023, data were extracted and transmitted from 10 sites for 35 patients, involving 367 clinical encounters across 15 clinical trials. Data through March 2023 demonstrated that concordance for the elements treatment plan change and cancer disease status was 79% and 34%, respectively. When disease evaluation was reported by both EHR and EDC (n=15), there was 87% agreement on cancer disease status. Documentation, extraction, and aggregation of structured data elements in EHRs using mCODE and ICAREdata methods is feasible in multi-institutional cancer clinical trials. EDC as a reference data setallowed assessment of the completeness of EHR data capture. Future initiatives will focus on elements with shared definitions in clinical and research environments and efficient workflows. Clinical trials use electronic case report forms to report data, and data must be manually entered on these forms, which is costly and time consuming. ICAREdata methods use structured, organized data from clinical trials that can be more easily shared instead having to enter free text into electronic health records.

CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model

Objective. The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients. Approach. A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans. Main Results. The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT. Significance. The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning.

Improving 3D dose prediction for breast radiotherapy using novel glowing masks and gradient-weighted loss functions.

The quality of treatment plans for breast cancer can vary greatly. This variation could be reduced by using dose prediction to automate treatment planning. Our work investigates novel methods for training deep-learning models that are capable of producing high-quality dose predictions for breast cancer treatment planning. The goal of this work was to compare the performance impact of two novel techniques for deep learning dose prediction models for tangent field treatments for breast cancer. The first technique, a "glowing" mask algorithm, encodes the distance from a contour into each voxel in a mask. The second, a gradient-weighted mean squared error (MSE) loss function, emphasizes the error in high-dose gradient regions in the predicted image. Four 3D U-Net deep learning models were trained using the planning CT and contours of the heart, lung, and tumor bed as inputs. The dataset consisted of 305 treatment plans split into 213/46/46 training/validation/test sets using a 70/15/15% split. We compared the impact of novel "glowing" anatomical mask inputs and a novel gradient-weighted MSE loss function to their standard counterparts, binary anatomical masks, and MSE loss, using an ablation study methodology. To assess performance, we examined the mean error and mean absolute error (ME/MAE) in dose across all within-body voxels, the error in mean dose to heart, ipsilateral lung, and tumor bed, dice similarity coefficient (DSC) across isodose volumes defined by 0%-100% prescribed dose thresholds, and gamma analysis (3%/3mm). The combination of novel glowing masks and gradient weighted loss function yielded the best-performing model in this study. This model resulted in a mean ME of 0.40%, MAE of 2.70%, an error in mean dose to heart and lung of -0.10 and 0.01Gy, and an error in mean dose to the tumor bed of -0.01%. The median DSC at 50/95/100% isodose levels were 0.91/0.87/0.82. The mean 3D gamma pass rate (3%/3mm) was 93%. This study found the combination of novel anatomical mask inputs and loss function for dose prediction resulted in superior performance to their standard counterparts. These results have important implications for the field of radiotherapy dose prediction, as the methods used here can be easily incorporated into many other dose prediction models for other treatment sites. Additionally, this dose prediction model for breast radiotherapy has sufficient performance to be used in an automated planning pipeline for tangent field radiotherapy and has the major benefit of not requiring a PTV for accurate dose prediction.

MiRNA-Targeted Vaccines: A Promising Approach for Viral Attenuation and Immunogenicity Enhancement.

MicroRNAs (miRNAs) have emerged as a significant tool in the realm of vaccinology, offering novel approaches to vaccine development. This study investigates the potential of miRNAs in the development of advanced vaccines, with an emphasis on how they regulate immune response and control viral replication. We go over the molecular features of miRNAs, such as their capacity to direct post-transcriptional regulation toward mRNAs, hence regulating the expression of genes in diverse tissues and cells. This property is harnessed to develop live attenuated vaccines that are tissue-specific, enhancing safety and immunogenicity. The review highlights recent advancements in using miRNA-targeted vaccines against viruses like influenza, poliovirus, and tick-borne encephalitis virus, demonstrating their attenuated replication in specific tissues while retaining immunogenicity. We also explored the function of miRNAs in the biology of cancer, highlighting their potential to develop cancer vaccines through targeting miRNAs that are overexpressed in tumor cells. The difficulties in developing miRNA vaccines are also covered in this work, including delivery, stability, off-target effects, and the requirement for individualized cancer treatment plans. We wrap off by discussing the potential of miRNA vaccines and highlighting how they will influence the development of vaccination techniques for cancer and infectious diseases in the future.

Evaluating the value of individualized 3D printed models for examination, diagnosis and treatment planning of cervical cancer

Background3D printing holds great potential of improving examination, diagnosis and treatment planning as well as interprofessional communication in the field of gynecological oncology. In the current manuscript we evaluated five individualized, patient-specific models of cervical cancer FIGO Stage I-III, created with 3D printing, concerning their value for translational oncology.MethodsMagnetic resonance imaging (MRI) of the pelvis was performed on a 3.0 Tesla MRI, including a T2-weighted isotropic 3D sequence. The MRI images were segmented and transferred to virtual 3D models via a custom-built 3D-model generation pipeline and printed by material extrusion. The 3D models were evaluated by all medical specialties involved in patient care of cervical cancer, namely surgeons, radiologists, pathologists and radiation oncologists. Information was obtained from evaluated profession-specific questionnaires which were filled out after inspecting all five models. The questionnaires included multiple-select questions, questions based on Likert scales (1 = „strongly disagree “ or „not at all useful “ up to 5 = „strongly agree “ or „extremely useful “) and dichotomous questions (“Yes” or “No”).ResultsSurgeons rated the models as useful during surgery (4.0 out of 5) and for patient communication (4.7 out of 5). Furthermore, they believed that the models had the potential to revise the patients’ treatment plan (3.7 out of 5). Pathologists evaluated with mean ratings of 3.0 out of 5 for the usefulness of the models in diagnostic reporting and macroscopic evaluation. Radiologist acknowledged the possibility of providing additional information compared to imaging alone (3.7 out of 5). Radiation oncologists strongly supported the concept by rating the models highly for understanding patient-specific pathological characteristics (4.3 out of 5), assisting interprofessional communication (mean 4.3 out of 5) and communication with patients (4.7 out of 5). They also found the models useful for improving radiotherapy treatment planning (4.3 out of 5).ConclusionThe study revealed that the 3D printed models were generally well-received by all medical disciplines, with radiation oncologists showing particularly strong support. Addressing the concerns and tailoring the use of 3D models to the specific needs of each medical speciality will be essential for realizing their full potential in clinical practice.

Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.

The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. The ensemble of models obtained the highest DSCavg of 0.84 ± 0.05 and IoUavg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan). The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.