Estimated Tumor Volume Research Articles

End-to-End Fully Automated Lung Cancer Volume Estimation System

The volume of the tumor plays a very crucial role in deciding the stage of lung cancer which in turn helps in deciding the best treatment and its schedule. Currently used computer-based volume estimation techniques are semi-automatic with limited accuracy. For any automatic lung cancer segmentation system, lung CT scans of hundreds of patients are required along with their corresponding annotated segmentation masks. It is difficult to get accurately annotated data as cancer segmentation of CT scans done by the radiologists, is a time-consuming manual process. Also, it is subjective and prone to intra and inter-observer variability. Further, owing to the irregular shape of the cancerous tumor, accurate volume estimation becomes a challenge with regular convolution models. This paper proposes an end-to-end automatic tumor volume estimation model that estimates volume using the GPR (Gaussian Process Regression) interpolation method. The proposed modified cancer segmentation model uses deformable convolutions. This modification offers a higher segmentation accuracy in terms of IoU (Intersection over Union) and clearly defined nodule boundaries with correct retention of the nodule shape. The research was undertaken in collaboration with Nanavati Hospital, Mumbai, and all the models were validated on a real dataset obtained from the hospital. The proposed model gives a mean segmentation IoU (Intersection over Union) of 0.9035 and a volume estimation accuracy of 93.13% which are almost 5% and 3% higher than 0.8548 and 90.51% which are the corresponding results obtained using a standard U-net++ algorithm.

Combination of S-1 and the oral ATR inhibitor ceralasertib is effective against pancreatic cancer cells.

In our previous study, we found that the Chk1 inhibitor prexasertib enhances the antitumour effect of the oral anticancer drug S-1 against pancreatic cancer cells. In this study, we investigated the effect of combining S-1 and ceralasertib, an oral inhibitor of ATR, which is located upstream of Chk1. Ceralasertib is currently being investigated in multiple clinical trials for various cancers. The cell-proliferation inhibitory effect was measured by MTT assay, using the pancreatic cancer cell lines BxPC-3, SUIT-2, PANC-1, and MIA PaCa-2, while apoptosis was measured by flow cytometry using PI/Annexin staining. The mechanism underlying the combined effect was analysed using western blotting, and the antitumor effect was analysed using a mouse xenograft model. MTT assay revealed that the combination of S-1 and ceralasertib had a synergistic effect, leading to the suppression of cell proliferation. Measurement with PI/Annexin staining revealed that the combination of S-1 and ceralasertib induced apoptosis more efficiently than either drug alone. Western blotting results showed that ceralasertib inhibited S-1-induced activation of ATR and Chk1. The average estimated tumour volume after 3weeks of administration was 601 mm3 in the S-1 group, 580 mm3 in the ceralasertib group, and 298 mm3 in the combination group. The combination of S-1 and ceralasertib demonstrated a high antiproliferative effect in inhibiting tumour growth in vitro.

Assessment of nurse practitioners’ capacity of using the ABC/2 formula for intracranial lesion volume measurement

BackgroundThis study investigates the precision of nurse practitioners (NPs) in measuring intracranial lesion volumes using the ABC/2 method, a simple yet widely used technique in neurosurgical practices. Amidst physician workforce shortages, the role of NPs in clinical practice, including specialized tasks like lesion volume estimation, is gaining importance.MethodsWe conducted a retrospective study involving patients treated for intracranial meningiomas. NPs estimated tumor volumes using the ABC/2 method, which was then compared with automated ABC/2 estimations considered as the gold standard. Statistical analyses, including paired sample t-tests, Bland-Altman analysis, and Intraclass Correlation Coefficient (ICC) analysis, were employed to assess measurement accuracy and consistency.ResultsAmong the 265 meningioma patients included, NPs measured the average tumor volume as 36.95 ml, generally underestimating it compared to the 39.57 ml average obtained by the automated ABC/2 method. This underestimation, however, was clinically modest, indicated by an average percentage difference of 6.59% and a Cohen’s d value of 0.08. Consistency in measurements, assessed using Bland-Altman and ICC analyses, demonstrated a high level of agreement between NPs measurements and the automated method. Additionally, no significant differences in measurement accuracy were observed either among different NPs or across NPs with varying levels of work experience.ConclusionsNurse practitioners can effectively employ the ABC/2 method for estimating intracranial lesion volumes with reasonable accuracy and consistency, irrespective of their work experience. This finding is pivotal in enhancing the role of NPs in neurosurgical practices and could be significant in alleviating the strain caused by the global shortage of physicians. Future research may explore extending NPs’ roles in other clinical diagnostic and therapeutic tasks.

P.081 Chordoma management with artificial intelligence: a scoping review of current applications and future prospects

Background: Chordomas are rare, malignant bone tumors that present significant challenges in management and treatment due to their complex anatomical locations and propensity for recurrence. Advancements in artificial intelligence (AI) and machine learning (ML) show promise in improving chordoma management. Methods: A comprehensive literature search was conducted following PRISMA guidelines across multiple databases, including MEDLINE, Cochrane, Embase, Scopus, and Web of Science. The search targeted articles related to AI and ML applications in clinical tasks associated with chordoma management. The selection process involved systematic screening, data extraction, and assessment of inter-rater variability. Results: The search yielded 1,006 records, with 18 included for analysis. Convolutional neural networks (CNNs) excelled in tumor volume estimation, with the state-of-the-art model achieving a Dice similarity score of 74.2%, sensitivity of 79.4%, and positive predictive value of 74.3%. Clustering algorithms were effective in prognostic evaluations. Bayesian models and logistic regression demonstrated robustness in diagnostics. Support vector machines (SVMs) were noted for their diagnostic precision. Conclusions: AI and ML algorithms, particularly CNNs, clustering algorithms, Bayesian models, logistic regression, and SVMs, show promise in improving chordoma management through enhanced imaging, diagnostics, and prognostics. Future research should focus on larger, externally validated datasets and explore underutilized techniques like multi-modal data integration.

The significance of partial volume effect on the estimation of hypoxic tumour volume with [18F]FMISO PET/CT

BackgroundThe purpose of this study was to evaluate how a retrospective correction of the partial volume effect (PVE) in [18F]fluoromisonidazole (FMISO) PET imaging, affects the hypoxia discoverability within a gross tumour volume (GTV). This method is based on recovery coefficients (RC) and is tailored for low-contrast tracers such as FMISO. The first stage was the generation of the scanner’s RC curves, using spheres with diameters from 10 to 37 mm, and the same homogeneous activity concentration, positioned in lower activity concentration background. Six sphere-to-background contrast ratios were used, from 10.0:1, down to 2.0:1, in order to investigate the dependence of RC on both the volume and the contrast ratio. The second stage was to validate the recovery-coefficient correction method in a more complex environment of non-spherical lesions of different volumes and inhomogeneous activity concentration. Finally, we applied the correction method to a clinical dataset derived from a prospective imaging trial (DRKS00003830): forty nine head and neck squamous cell carcinoma (HNSCC) cases who had undergone FMISO PET/CT scanning for the quantification of tumour hypoxia before (W0), 2 weeks (W2) and 5 weeks (W5) after the beginning of radiotherapy. Here, PVE was found to cause an underestimation of the activity in small volumes with high FMISO signal.ResultsThe application of the proposed correction method resulted in a statistically significant increase of both the hypoxic subvolume (171% at W0, 691% at W2 and 4.60 × 103% at W5 with p < 0.001) and the FMISO standardised uptake value (SUV) (27% at W0, 21% at W2 and by 25% at W5 with p < 0.001) within the primary GTV.ConclusionsThe proposed PVE-correction method resulted in a statistically significant increase of the hypoxic fraction (HF) with p < 0.001 and demonstrated results in better agreement with published HF data for HNSCC. To summarise, the proposed RC-based correction method can be a useful tool for a retrospective compensation against PVE.

Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects

BackgroundWhole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects.MethodsAn extensive PubMed search was conducted until February 26, 2023, with the search term “prostate”, “whole-mount”, “large format histology”, which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding.ResultsWMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region.ConclusionsWMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.

Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma

ObjectivesLung metastases in adenoid cystic carcinoma (ACC) usually have indolent growth and the optimal timing to start systemic therapy is not established. We assessed ACC lung metastasis tumor growth dynamics and compared the prognostic value of time to progression (TTP) and tumor volume doubling time (TVDT). MethodsThe study included ACC patients with ≥1 pulmonary metastasis (≥5 mm) and at least 2 chest computed tomography scans. Radiology assessment was performed from the first scan showing metastasis until treatment initiation or death. Up to 5 lung nodules per patient were segmented for TVDT calculation. To assess tumor growth rate (TGR), the correlation coefficient (r) and coefficient of determination (R2) were calculated for measured lung nodules. TTP was assessed per RECIST 1.1; TVDT was calculated using the Schwartz formula. Overall survival was analyzed using the Kaplan-Meier method. ResultsThe study included 75 patients. Sixty-seven patients (89%) had lung-only metastasis on first CT scan. The TGR was overall constant (median R2 = 0.974). Median TTP and TVDT were 11.2 months and 7.5 months. Shorter TVDT (<6 months) was associated with poor overall survival (HR = 0.48; p = 0.037), but TTP was not associated with survival (HR = 1.02; p = 0.96). Cox regression showed that TVDT but not TTP significantly correlated with OS. TVDT calculated using estimated tumor volume correlated with TVDT obtained by segmentation. ConclusionMost ACC lung metastases have a constant TGR. TVDT may be a better prognostic indicator than TTP in lung-metastatic ACC. TVDT can be estimated by single longitudinal measurement in clinical practice.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.

Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients. This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant. No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively. The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC. First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.

Evaluation of superficial xenograft volume estimation by ultrasound and caliper against MRI in a longitudinal pre-clinical radiotherapeutic setting.

Accurate tumor volume estimation is important for evaluating the response to radionuclide therapy and external beam radiotherapy as well as to other pharmaceuticals. A common method for monitoring the growth of subcutaneous tumors in pre-clinical models and assessing the treatment response is to measure the tumor length and width by external calipers to estimate its volume. This procedure relies on an assumption of a spheroidal tumor shape wherein the tumor depth equals the width and can yield considerably inaccuracies. Ultrasound imaging is a non-invasive technique that can measure all three axes of the tumor and might be an alternative to caliper measurement with potentially greater accuracy and comparable ease-of-use and throughput. Both 2D and 3D ultrasound imaging are possible, the former offering short scan times without the need for anesthesia and heating-valuable factors for longitudinal studies in large animal cohorts. Nevertheless, tumor volume estimation accuracy by 2D ultrasound imaging has seen limited investigation. In this study we have evaluated the accuracy of tumor volume estimation by caliper and 2D ultrasound with comparisons to reference measurements by magnetic resonance imaging (MRI) in a pre-clinical model of prostate cancer treated with either external beam radiotherapy, radionuclide therapy, or no treatment. Tumor volumes were measured longitudinally in 29 mice by caliper, ultrasound, and MRI before and after external beam radiotherapy, [177Lu]Lu-PSMA-617 radionuclide therapy, or no treatment. Caliper measurements had a marked bias, overestimating the tumor volumes by a median of 150% compared to MRI. Ultrasound measurements were markedly more accurate, with a median bias of -21% compared to MRI. Ultrasound imaging is a reliable and accurate method for tumor volume estimation in pre-clinical models of radiotherapy, whereas caliper measurements are prone to overestimation.

Three-Dimensional Ultrasound Imaging of Retinoblastoma and Its Correlation With Pathology.

Monitoring the response of retinoblastoma to globe-salvaging therapies is based on subjective assessments of changes determined by fundoscopy, ultrasound, and optical coherence tomography. Advances in organ-preserving therapies have increased the need for objective, quantitative estimates of tumor response to treatment. Primary tumor volume is a metric that can be objectively determined as a surrogate measure of treatment response. We evaluated the correlation of objective, quantitative estimates of tumor volume made with two-dimensional (2D) and three-dimensional (3D) ultrasound with gold standard pathological tumor volumes derived by analysis of enucleation specimens. Twelve eyes in 12 patients undergoing primary enucleation were evaluated by 2D and 3D ultrasound during ophthalmic examination under anesthesia prior to enucleation. 2D- and 3D-ultra-sound measurements of tumor volume were both strongly correlated with pathological estimates of tumor volume (r = 0.69, P = 0.018; and r = 0.66, P = 0.027, respectively). 2D- and 3D-ultrasound measurements of retinoblastoma primary tumor volume are highly correlated with pathological estimates. 3D measurements are easy to perform with volumetric probes and consider the irregular morphology of the tumor. Further study should be undertaken to evaluate the performance of these metrics as surrogate markers of tumor response to treatment. [Ophthalmic Surg Lasers Imaging Retina 2024;55:136-140.].

Diffuse low-grade glioma: What is the optimal linear measure to assess tumor growth?

Radiological follow-up of diffuse low-grade gliomas (LGGs) growth is challenging. Approximative visual assessment still predominates over objective quantification due to the complexity of the pathology. The infiltrating character, diffuse borders and presence of surgical cavities demand LGG-based linear measurement rules to efficiently and precisely assess LGG evolution over time. We compared optimized 1D, 2D, and 3D linear measurements with manual volume segmentation as a reference to assess LGG tumor growth in 36 patients with LGG (340 magnetic resonance imaging scans), using the clinically important mean tumor diameter (MTD) and the velocity diameter expansion (VDE). LGG-specific progression thresholds were established using the high-grade gliomas-based RECIST, Macdonald, and RANO criteria, comparing the sensitivity to identify progression/non-progression for each linear method compared to the ground truth established by the manual segmentation. 3D linear volume approximation correlated strongly with manually segmented volume. It also showed the highest sensitivity for progression detection. The MTD showed a comparable result, whereas the VDE highlighted that caution is warranted in the case of small tumors with multiple residues. Novel LGG-specific progression thresholds, or the critical change in estimated tumor volume, were increased for the 3D (from 40% to 52%) and 2D methods (from 25% to 33%) and decreased for the 1D method (from 20% to 16%). Using the 3D method allowed a ~5-minute time gain. While manual volumetric assessment remains the gold standard for calculating growth rate, the 3D linear method is the best time-efficient standardized alternative for radiological evaluation of LGGs in routine use.

Advancing total tumor volume estimation in colorectal liver metastases: development and evaluation of a self-learning auto-segmentation model

Advancing total tumor volume estimation in colorectal liver metastases: development and evaluation of a self-learning auto-segmentation model

Improved Staging of Ciliary Body and Choroidal Melanomas Based on Estimation of Tumor Volume and Competing Risk Analyses

The current, 8th edition of the American Joint Committee on Cancer (AJCC) anatomic classification and staging model for uveal melanoma does not fully separate survival estimates for patients with advanced stages of the disease (e.g., IIIB and IIIC). Furthermore, some tumors in higher size categories have a smaller volume than tumors in lower categories. Therefore, we developed a novel model for prognostication of metastatic mortality based on estimations of tumor volume. Retrospective, multicenter case series of patients with uveal melanoma involving the choroid, ciliary body, or both. Six thousand five hundred twenty-eight consecutively registered patients treated at 3 tertiary ocular oncology centers on 2 continents between 1981 and2022. Data on survival, tumor size, and extent were collected for all 6528 patients. Tumor volume was estimated using a simple equation based on largest basal diameter and thickness. Volume-based size categories and stages were developed and validated in independent patient cohorts using competing risk analyses, and correlations with cytogenetic and cytomorphologic features were examined. Cumulative incidence of metastatic death. The 6528 patients were distributed over 7 stages based on estimated tumor volume and anatomic extent (V stages IA, IB, IIA, IIB, IIIA, IIIB, and IIIC), with a 15-year incidence of metastatic death ranging from 7% to 77%. A new category, V1min, and corresponding stage IA, were introduced, indicating an excellent prognosis. Metastatic mortality in V stage IIIC was significantly higher than that in V stage IIIB (P= 0.03), whereas incidence curves crossed for patients in AJCC stages IIIC vs. IIIB (P= 0.53). Univariable and multivariable competing risk regressions demonstrated higher Wald statistics for V stages compared with AJCC stages (1152 vs. 1038 and 71 vs. 17, respectively). The frequency of monosomy 3, gain of chromosome 8q, and epithelioid cytomorphologic features increased with tumor volume (R2= 0.70, R2= 0.50, and R2= 0.71, respectively; P < 0.001) and showed similar correlations with both AJCC and V stages. Anatomic classification and staging of ciliary body and choroidal melanomas based on estimation of tumor volume improves prognostication of metastatic mortality. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors

Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histopathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm’s annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity, 100%). Preanalytical factors had no significant impact on whole slide imaging and subsequent application of a CADe software. Prediction accuracy could potentially be further improved by processing biopsy specimens in a centralized histology laboratory and training the Algorithm on WSIs from the same laboratory in order to minimize variations in preanalytical factors and optimize the diagnostic performance of the Algorithm.

Radio-Immune Response Model for Radiotherapy Plans with Heterogeneous Dose Distribution

In order to model the immune response in tumor of the patients under radiotherapy for cancer. Characteristics of the numerical model and preliminary application are presented. Immune response was modelled by 4 set of ordinary difference equations (ODE) as a function of biomedical variables including the amount of tumor antigen naturally released by tumor, the activation of cytotoxic T-lymphocytes (CTL) by radiation, immune suppression by tumor volume and the use of immunotherapy drugs. The effect of heterogeneous radiation dose distribution is also considered by hyperbolic tangent function to account for the immunogenic response of tissue under highly heterogeneous dose distribution intentionally modulated in spatially fractionated radiotherapy. Boundary behaviors of the model were investigated for tumors with different biomedical characteristics and under different treatment conditions. The developed model was applied to the tumor volume change in a mouse with 67NR tumors after radiation of 10 Gy to full or half volume of tumor and a clinical patient treated for sarcoma three times over 4 years. Tumor growth is exponential at early phase, slow down over time with increasing immune response and eventually reaches an equilibrium condition (known as terminal tumor volume) for tumors with little to no immune suppression capability (ISC) even in the lack of radiation treatment. Breaking-through the equilibrium for tumor to grow exponentially happens when ISC is larger than the bifurcation threshold, analytically calculated from the proposed model. Tumor with ISC close to the bifurcation threshold can show complex growth behavior depending on the treatment condition and it should be carefully considered for the optimal treatment. Tumor volume change over 30 days period on mouse was modeled well with this model. Full dose irradiation reduced the tumor volume faster in the first 10 days but half volume irradiation reduced the tumor volume faster at later stage due to the improved immune response which cannot be modelled with traditional linear quadratic model. Tumor volume on a patient retreated three times over 4 years was also accurately estimated. The proposed immune response model is capable to estimate tumor volume response under full or partial volume irradiation considering the complex immune characteristics of the tissue.

Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis

In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively.

Conservative surgical approaches for small volume FIGO stage IB1 cervical cancer. Updated survival and obstetric outcomes of an expanded cohort

ObjectiveStandard surgical treatment of FIGO stage 1B1 cervical cancer is open radical surgery. However, there is increasing evidence that for small tumours a more conservative approach can minimise fertility consequences without impacting on long term oncologic outcomes. The objective of our study is to present survival and obstetric outcomes following extended follow-up for patients who underwent conservative management of small-volume stage 1B1 disease. MethodsAll patients with FIGO stage 1B1 cancer and estimated tumour volume of <500 mm3 in a loop biopsy specimen treated in Northern Gynaecological Oncology Centre between December 2000 and December 2021, were included in the study. Clinico-pathological and demographic data were collated alongside detailed follow-up and obstetric outcomes in conjunction with primary care and death register. Results117 patients underwent conservative surgery for small volume stage 1B1 disease. 58 (49.5%) underwent fertility sparing conservative management with LLETZ while 59 (50.5%) underwent simple hysterectomy. Overall, 95% (111/117) of the patients underwent bilateral pelvic lymphadenectomy and 1 positive node was identified. There was no death related to cervical cancer and 1 recurrence identified during a median follow up of 8.5 years (1−20). 17 pregnancies have been recorded in patients underwent LLETZ and 17 live babies were born. No second trimester miscarriages were noted and there was one preterm delivery (36 weeks). ConclusionNon-radical surgery with negative pelvic lymphadenectomy for smallvolume stage 1B1 cervical cancer ensures excellent survival without compromising obstetric outcomes. Should these results be verified by the ongoing prospective studies, radical surgery for these patients may be avoided.

Role of extent of resection on the survival of glioblastoma multiforme

Background. Glioblastoma Multiforme (GBM) is the most prevalent form of brain cancer. The effect of Extent of Resection (EOR) on GBM survival is controversial. EOR degree, pre- and postoperative tumour volume estimation, and significance to residual tumour volume are still challenged. GBM has a 14-month Overall Survival (OS) rate. There is no evidence of a link between EOR and OS survival. We wish to determine whether GBM tumour removal increases survival. Methods. At the Regional Center for Neurosurgery and Neurology in Uzhhorod, Ukraine, we conducted a retrospective evaluation of 86 consecutive patients diagnosed with glioblastoma who underwent surgery between January 1, 2010, and December 31, 2020, and who are being followed until January 1, 2022. Patients were selected if they met the following criteria: they were at least 18 years old, they had a diagnosis of glioblastoma (primary, secondary, or recurrent), they were either IDH mutants or wild types, they had an MRI within 2 weeks before surgery, and they had another MRI within 72 hours after surgery. Before and after surgery, we did a volumetric analysis of gadolinium-enhanced T1 MRI scans of the tumour to figure out EOR. Partial resection (PR) is &lt;70%, sub-total resection (STR) is 70-90%, near-total resection (NTR) is 91-99%, and gross total resection (GTR) is &gt;99%. By comparing pre- and post-operative volumes with the EOR, the Kaplan-Meier survival curve and Cox's regression analysis determined the impact of the EOR on survival rates. Many researchers considered a p value of 0.05 or below to be significant. Results. A total of 86 patients were included in the analysis after being subjected to the criteria used to narrow the pool of potential participants. The average length of time people lived was 15 months. For PR patients, the median survival time was 3 months, for STR patients it was 10 months, and for NTR patients it was 16 months. Patients receiving GTR, on the other hand, had a considerably better outcome, with a median survival time of 36 months. This data demonstrate a direct correlation between EOR and survival rates. It was discovered that EOR improvement affected post-op survival. High EOR patients have a better prognosis for survival. Adjuvant therapy, pre- and post-operative KPS score, pre- and post-operative tumour volume, and gender also contributed significantly to enhanced survival. Conclusion. Patients with glioblastoma appear to benefit from a more aggressive treatment strategy that combines maximal safe resection with the use of salvage adjuvant therapy. There was a correlation between complete resection (gross total resection) of intracranial GBM and improved survival. Whenever feasible, complete surgical removal of the tumour is recommended.

Measuring pituitary tumor volume: a comparison of the simplified and non-simplified ellipsoid equation with the 3D planimetric volume assessment.

The ellipsoid equation came in an attempt to facilitate the estimation of tumor volume, by measuring the height, width, and anteroposterior length of the lesion. The estimated tumor volume can sometimes be different between methods, thus, it is of extreme interest to evaluate if the methods are significantly different, as well as to discuss the main limitations of each one. This is an observational, analytical, cross-sectional study. A systematic review of the literature was also performed in order to discuss the results observed in the present study. A total of 82 patients (43 males and 39 females) ranging in age from 15 to 78 years (mean 47.95 ± 14.76) were included in the study. Seven patients were classified as Knosp grade 0 (8.5%), 36 Knosp grade 1 (44%), 14 Knosp grade 2 (17%), 20 Knosp grade 3 (24.4%), 5 Knosp grade 4 (6.1%). The tumor volume estimated by 3D planimetric assessment, non-simplified ellipsoid equation, and simplified ellipsoid formula averaged 10.68cm³, 10.36cm³, and 9.9cm³ respectively. A simplified form of the ellipsoid equation increases the divergence between the measurement obtained in planimetry, and should be discouraged, in view of the new automated methods of performing quick calculations using periodic digits. The non-simplified form underestimated the tumor volume by 2.9% on average but did so regularly. In clinical practice, measurement should be accompanied by an evaluation of tumor morphology.