Tumor Quantification Research Articles

Effect of Beta- and Alpha-Carotene Intake on Tumour Formation in the Intestines of APCMin/+ Mice

Carotene-rich vegetables like carrots have been consistently associated with lowered cancer risk in observational studies(1). Beta-carotene, a phytochemical found in fruits and vegetables, is known for its antioxidant properties. In rodent trials, supplementation with carrot (containing both carotenes and polyacetylenes) significantly reduced tumour numbers in the APCMin/+ mouse model(2). However, human intervention trials with pure beta-carotene showed more adverse effects than benefits(3). This study aimed to investigate the impact of beta- and alpha-carotene supplementation (without polyacetylenes) on colorectal cancer using the APCMin/+ mouse model, which spontaneously develops intestinal tumours and an enlarged liver(4).APCMin/+ mice were fed either a daily supplement of 10mg/kg mouse of a polyacetylene-free extract from carrots containing a beta- and alpha-carotene mixture, in a dose corresponding to the beneficial carrot supplementation, or the control diet, with a very low beta-carotene content (providing approx. 0.02mg/kg mouse/day). The 8-week supplementation commenced at 5 weeks of age. Tumour quantification in the small intestines of APCMin/+ mice was performed by histology of the entire small intestine, with one slide per 0.3mm, using the H&E staining method and Imagescope software.Supplementation with carotenes had no significant effect on the total number of tumours (≥0.5 mm), showing no discernible difference between the control (n = 6) (20.5, 95% CI [14.69, 26.31]) and treatment (n = 8) (24.5, [19.21, 29.79]) group (P = 0.237). However, the liver weight, in % of body weight, showed a significant 25% decrease in the treatment group (n = 7) (4.78 95% CI [4.45, 5.15]) compared to controls (6.42, [5.71, 7.13]) (P<0.0003), reaching a level comparable with wildtype mice (4.48, [4.13, 4.83].These findings confirm the results from the human intervention trials by indicating that beta- and alpha-carotene do not confer any benefit for cancer prevention, while the decrease in liver weight suggests a potential beneficial effect on another aspect of liver health. Additional research is necessary to fully understand these relationships and uncover the underlying mechanisms behind these results. Specifically, beta-and alpha-carotene from carrots do not provide the reduction in tumour numbers in a mouse model of colon cancer found when feeding the corresponding dose of freeze-dried carrot. However, the carotenes significantly attenuate the liver enlargement also seen in this model.

Neoadjuvant Therapy and Lung Cancer: Role of Pathologists.

Recent neoadjuvant clinical trials in lung cancer have demonstrated the survival benefits in carefully selected patients. Standardization of the assessment of pathologic response to neoadjuvant therapy in surgically resected specimens is required. To review the current pathology practices in the gross processing and microscopic assessment of surgically resected non-small cell lung carcinoma specimens after neoadjuvant therapy. PubMed publications and experience of the author. Gross processing of the surgically resected lung carcinoma after neoadjuvant therapy needs further refinement and standardization in clinical trials and in a real-world clinical practice. Microscopic assessment of the response includes quantification of viable tumor, necrosis, and stroma. The best approach would be to use a single standardized and most reproducible scoring system. Published studies on gross processing of lung carcinoma specimens in the neoadjuvant setting and microscopic assessment of pathologic response provide a good foundation for the future standardization of pathology practice.

Specific isolation and quantification of PD-L1 positive tumor derived exosomes for accurate breast cancer discrimination via aptamer-functionalized magnetic composites and SERS immunoassay

PD-L1 positive tumor derived exosomes (TEXsPD−L1) play a significant role in disease progression, tumor metastasis and cancer immunotherapy. However, the overlap of PD-L1 between TEXs and non-tumor derived exosomes (non-TEXs) restricts the specific isolation and quantification of TEXPD−L1 from clinical samples. Herein, a new aptamer-functionalized and hydrophilic immunomagnetic substrate was designed by decorating generation 5 polyamidoamine dendrimers (G5 PAMAM), zwitterionic trimethylamine N-oxide (TMAO) and EpCAM (Epithelial cell adhesion molecule) aptamers on magnetic cores sequentially (Fe3O4@PAMAM@TMAO@Aptamer, named as FPTA) for rapid target and efficient capture of TEXs. The FPTA substrate gathered excellent characters of strong magnetic responsiveness of Fe3O4, abundant affinity sites of PAMAM, strong hydrophilicity of TMAO and enhanced affinity properties of EpCAM aptamers. Because of these advantages, FPTA can isolate TEXs quickly within 30min with high capture efficiency of 90.5 % ± 3.0 % and low nonspecific absorption of 8.2 % ± 2.0 % for non-TEXs. Furthermore, PD-L1 (Programmed cell death-ligand 1) positive TEXs (TEXsPD−L1) from the captured TEXs were recognized and quantitatively analyzed by utilizing SERS (surface-enhanced Raman spectroscopy) reporter molecules 4-NTP (4-Nitrothiophenol) on PD-L1 aptamers-functionalized gold immunoaffinity probe. The signal of TEXsPD−L1 was converted to SERS signal of 4-NTP at 1344 cm−1 which exhibited a linear correlation to concentration of TEXsPD−L1(R2 = 0.9905). With these merits, this strategy was further applied to clinical plasma samples from breast cancer (BC) patients and healthy controls (HC), exhibited an excellent diagnosis accuracy with area under curve (AUC) of receiver operating characteristic (ROC) curve reaching 0.988. All these results demonstrate that the FPTA immunomagnetic substrate combined with SERS immunoaffinity probe may become a generic tool for specific isolation and quantitative analysis of PD-L1 positive tumor-derived exosomes in clinics.

A Review of Evaluating Deep Learning Techniques in Hepatic Cancer Imaging: Automated Segmentation and Tumor Quantification

A Review of Evaluating Deep Learning Techniques in Hepatic Cancer Imaging: Automated Segmentation and Tumor Quantification

Breast lesions segmentation from ultrasound images using DeepLabV3 + model with channel and spatial attention mechanism

Segmenting breast tumors in ultrasound images is pivotal for computer-aided diagnosis (CAD) systems aimed at detecting breast cancer. This research contributes directly to the Sustainable Development Goal (SDG), good health and well-being by leveraging innovative deep learning technique to boost the accuracy of breast tumor segmentation. Achieving accurate segmentation is crucial for precise tumor size, shape, and location determination, which in turn facilitates subsequent tumor quantification and classification. However, this task presents challenges, particularly when dealing with small tumors. The complexity arises from factors such as speckle noise, variations in tumor shapes and sizes across patients, and the presence of tumor-like regions in the images. Although deep learning-based methods have shown remarkable success in biomedical image analysis, the current state-of-the-art approaches still struggle to achieve satisfactory performance in segmenting small breast tumors. In this paper, BUS (Breast Ultrasound) dataset was initially applied on state of-the art DeepLabV3 + architecture for the segmentation of breast lesions in ultrasound images. After that to focus on more informative features, a modified DeepLabV3 + has been proposed by integrating a CBAM module in encoder as well as decoder. At the end, comparative analysis of state-of-art DeepLabV3 + model and proposed modified DeepLabV3 + has also been presented in terms of performance metrics like Dice Coefficient, Intersection over Union, Precision, Recall and Specificity. The proposed model demonstrates outstanding performance across different evaluation metrics achieving precision, recall, specificity, dice coefficient and Intersection over Union (IoU) values of 0.974, 0.933, 0.997, 0.951, and 0.933. respectively.

Confocal microscopic oxygen imaging of xenograft tumors using Ir(III) complexes as in vivo intravascular and intracellular probes

Hypoxia is an important feature of the tumor microenvironment (TME) of most solid tumors, and it is closely linked to cancer cell proliferation, therapy resistance, and the tumor immune response. Herein, we describe a method for hypoxia-induced heterogeneous oxygen distribution in xenograft tumors based on phosphorescence imaging microscopy (PLIM) using intravascular and intracellular oxygen probes. We synthesized Ir(III) complexes with polyethylene glycol (PEG) units of different molecular weights into the ligand as intravascular oxygen probes, BTP-PEGm (m = 2000, 5000, 10000, 20000). BTP-PEGm showed red emission with relatively high emission quantum yield and high oxygen sensitivity in saline. Cellular and in vivo experiments using these complexes revealed that BTP-PEG10000 was the most suitable probe in terms of blood retention and ease of intravenous administration in mice. PLIM measurements of xenograft tumors in mice treated with BTP-PEG10000 allowed simultaneous imaging of the tumor microvasculature and quantification of oxygen partial pressures. From lifetime images using the red-emitting intracellular oxygen probe BTPDM1 and the green-emitting intravascular fluorescent probe FITC-dextran, we demonstrated hypoxic heterogeneity in the TME with a sparse vascular network and showed that the oxygen levels of tumor cells gradually decreased with vascular distance.

PET quantification performance of the oversize-volume-of-interest approach in the context of tumour dosimetry in radionuclide therapy planning

Objective. The partial-volume effect (PVE) is an important factor impairing tumour quantification in molecular imaging. The commonly used contour-volume-of-interest (contour-VOI) approach to correct for this effect employs phantom-based recovery coefficients. Applying oversize-VOIs could offer superior quantification accuracy in small lesions. The oversize-VOI approach uses a large oversize volume to determine the total tumour activity after applying a background correction. Aims of this study were to provide a procedure for the application of the oversize-VOI approach and to compare its performance to the contour-VOI approach in PET imaging. Approach. A sphere tumour model was simulated to determine the oversize diameter that contained 90%, 95%, and 98% of the total activity as a function of the tumour size. Experimental investigations involving phantom and clinical data were conducted on a digital PET/CT scanner. In the phantom investigation, 12 spherical tumour inserts (diameters ranging from 3.7 to 37.4 mm) containing 18F-solution were used. The accuracy of the contour- and oversize-VOI approach was evaluated for different signal-to-background ratios (20–3). Clinically, both approaches were applied on PET/CT images acquired with 18F-labelled prostate-specific membrane antigen in prostate cancer patients. Main results. From the tumour model, we deduced that an oversize-VOI of two PET spatial resolutions larger than the physical lesion diameter contains at least 98% of the total activity for lesions with diameters down to one PET spatial resolution, while minimizing the background contribution. Both approaches were robust against varying phantom and clinical imaging conditions. Performance of the oversize-VOI approach was favorable for lesions below 10 mm in diameter, whereas the contour-VOI approach was slightly more accurate for sizes above 10 mm. Significance. The oversize-VOI approach facilitates image quantification of small tumours. It is simple and effective to correct for the PVE and may be used in pre-therapeutic (small) tumour dosimetry.

Prognostic value of HPV circulating tumor DNA detection and quantification in locally advanced cervical cancer.

Cervical cancers are mainly caused by an oncogenic HPV. For locally advanced stages, the standard treatment is radio-chemotherapy (RTCT) followed by brachytherapy. Nevertheless, the prognosis remains highly heterogeneous between patients. We investigated the prognostic value of HPV circulating tumor DNA (ctDNA) in locally advanced cervical cancers alongside that of Squamous Cell Carcinoma Antigen (SCC-A). This single-center retrospective study included patients treated in curative intent for an IB3 to IVA squamous cell cervical cancer. Quantification of HPV ctDNA in serum collected at diagnosis was performed using a multiplex digital PCR assay for the simultaneous detection of 8 HPV genotypes. Among the 97 patients included, 76 patients (78.4%) were treated by RTCT, followed by brachytherapy for 57 patients (60%). HPV ctDNA was detected in 59/97 patients at diagnosis (60.8%). This detection was associated with lymph node invasion (p=0.04) but not with tumor stage. A high level of SCC-A at diagnosis was associated with tumor stage (p=0.008) and lymph node invasion (p=0.012). In univariate analysis, better disease-free survival (DFS) was associated with optimal RTCT regimen (p=0.002), exposure to brachytherapy (p=0.0001) and a low SCC-A at diagnosis (continuous analysis, p=0.002). Exploratory analysis revealed that 3/3 patients (100%) whose HPV ctDNA was still detectable at the end of treatment relapsed, while 6/22 patients (27.3%) whose HPV ctDNA was negative at the end of treatment relapsed. HPV ctDNA detection at diagnosis of locally advanced cervical squamous cell carcinomas is frequent and related to node invasion, but not to DFS. The prognostic value of HPV ctDNA detection after treatment warrants specific studies.

Extensive-stage small-cell lung cancer in patients receiving atezolizumab plus carboplatin–etoposide: stratification of outcome based on a composite score that combines gene expression profiling and immune characterization of microenvironment

PurposeSmall-cell lung cancer (SCLC) is an aggressive disease with a dismal prognosis. The addition of immune checkpoints inhibitors to standard platinum-based chemotherapy in first-line setting achieves a durable benefit only...

Abstract PO5-03-08: Detection and quantification of triple-negative breast cancer (TNBC) across ethnicities through analysis of cell-free DNA (cfDNA) methylation

Abstract Introduction: Triple-negative breast cancer (TNBC) is an aggressive subtype contributing to race-related outcome disparities in breast cancer. These disparities are partly explained by the higher burden of TNBC in Black women, less access to new mammography technologies, and further compounded by limited efficacy of screening mammography in early detection of TNBC (Newman L & Mitchell E, J Natl Med Assoc 2023). To better understand these disparities, we compared results from the Galleri® multi-cancer early detection (MCED) test and the tumor methylated fraction (TMeF) tumor quantification algorithm in a population of TNBC patients across different stages and self-reported ethnicities (SRE). Methods: Within the case-control Circulating Cell-free Genome Atlas third substudy, we performed MCED testing and TMeF estimation in 507 women with breast cancer, 84 of whom had TNBC. The MCED test detects methylation patterns associated with circulating tumor DNA (ctDNA) to identify a common cancer signal and predict a cancer signal of origin. Tumor methylated fraction (TMeF) is an estimate of ctDNA abundance using machine learning models trained on whole-genome bisulfite sequencing of tumor biopsy tissue. We used SRE to compare results between non-Hispanic Black and White participants and those of other (Hispanic, Asian, Pacific Islander) or unreported ethnicities for both cancer detection by MCED test and ctDNA abundance estimation by TMeF. Results: In Black women, 19 (49%) of 39 diagnosed breast cancers were TNBC, compared to 58 (15%) of 396 breast cancers in White women and 7 (13%) of 52 breast cancers in women of other or unreported ethnicities. The MCED test did not detect Stage I TNBC (Table 1). In patients with Stage II-IV TNBC, the MCED test successfully detected a cancer signal in 16 (94%) of 17 Black women, 31 (72%) of 43 White women, and 6 (100%) of 6 women of other or unreported ethnicities (Table 1). When we examined the TMeF in these patients using a two-way ANOVA, we found a significant difference in mean TMeF by cancer stage (p < 0.001) but no significant difference in mean TMeF between SRE groups (p = 0.09) with the distribution of TMeF (Table 2). The ANOVA additionally found a significant interaction between cancer stage and SRE (p < 0.001) that could represent an impact of stage on TMeF that varied by SRE or differences in cancer stage distribution by SRE. Conclusions: In this pilot study, we observed that MCED testing was effective in detecting Stage II, III, and IV TNBC across SRE. These findings, in combination with the association of estimated ctDNA abundance with cancer stage but not patient SRE, provide hypothesis-generating evidence that analysis of methylation signals from across the genome can provide meaningful TNBC screening/detection information, independent of self-reported race/ethnicity. Table 1. MCED cancer detection rate by stage and SRE among TNBC patients. Table 2. Tumor methylated fraction (TMeF) by stage and SRE among TNBC patients. Citation Format: William Cance, Edison Liu, Margaret Antonio, Timothy Shaver, Stephannie Shih, Bong Chul Chu, Kathryn Kurtzman, Lisa Newman. Detection and quantification of triple-negative breast cancer (TNBC) across ethnicities through analysis of cell-free DNA (cfDNA) methylation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-08.

Abstract CT125: A phase 0/2 ‘trigger’ trial of pamiparib or olaparib plus radiotherapy in patients with newly-diagnosed or recurrent glioblastoma

Abstract Background: This Phase 0/2 clinical trial evaluates the pharmacokinetic (PK), pharmacodynamic (PD), and clinical response of newly-diagnosed and recurrent glioblastoma (GBM) patients to the PARP1/2 selective inhibitors, pamiparib and olaparib, in combination with radiotherapy. Materials/Methods: In this study, newly-diagnosed (Arm A) and recurrent GBM (Arm B) patients received 4 days of pamiparib (60 mg BID) prior to planned resection at 2-4 or 8-12 hours following the final dose. Arm C included recurrent GBM patients who received 4 days of olaparib (200 mg BID) prior to resection. In the Phase 0 component of the study total and unbound drug concentrations were measured using validated LC-MS/MS methods. A PK ‘trigger’, defined as unbound drug > 5-fold biochemical IC50 in Gd-nonenhancing tumor, determined eligibility for Phase 2. PARP inhibition was assessed via ex vivo tumor tissue radiation and quantification of PAR levels compared to non-irradiated control. When demonstrating a PK response, newly-diagnosed, MGMT-unmethylated GBM or recurrent GBM patients were eligible for Phase 2 dosing of pamiparib (Arms A and B) or olaparib (Arm C) with concurrent RT followed by maintenance pamiparib or olaparib. Results: A total of 41 patients (Arm A, n=16; Arm B, n=19; Arm C, n=6) were enrolled in the Phase 0 component of the study. In Arms A and B, mean unbound concentrations of pamiparib in Gd-nonenhancing tumor were 167.3 nM and 104.3 nM respectively, and in Arm C the mean unbound concentration of olaparib was 5.2 nM. All patients in the pamiparib arms (n=35) but only 1 of 6 patients in the olaparib Arm C exceeded the PK threshold. Radiation-induced PAR expression was 2.44-fold in untreated control vs 1.16 in Arm A (p<0.05), 0.82 in Arm B (p<0.01) and 1.11 in Arm C patients. In Arm A, 7 of 11 MGMT-unmethylated GBM patients advanced to Phase 2. Two patients were ineligible due to clinical status, 1 patient was ineligible per physician decision, and 1 patient declined to participate. In Arm B, 11 of 19 patients advanced to Phase 2. Eight patients were ineligible due to clinical status. At a mean follow-up of 5.8 months, median progression-free survival (PFS) was 5.8, 6.0, and 3.8 months for Arms A (n=7), B (n=11), and C (n=1), respectively. Grade 3+ toxicities related to pamiparib occurred in 4 patients, with 2 adverse events resulted in treatment discontinuation. No grade 3+ toxicities were documented in the olaparib arm. Conclusions: Pamiparib achieved pharmacologically-relevant concentrations in Gd-nonenhancing GBM tissue and suppressed induction of PAR levels ex vivo post-radiation. The majority of newly-diagnosed patients with MGMT-unmethylated GBM and recurrent GBM patients advanced to therapeutic dosing in this trial, and pamiparib was generally well-tolerated in these patients. Citation Format: Nader Sanai, Yu-Wei Chang, Jun Jiang, Jennifer Molloy, Chelsea Pennington-Krygier, Jocelyn Harmon, Amy Hong, John Wanebo, William Kennedy, Michael Garcia, Igor Barani, Wonsuk Yoo, Artak Tovmasyan, An-Chi Tien, Jing Li, Shwetal Mehta. A phase 0/2 ‘trigger’ trial of pamiparib or olaparib plus radiotherapy in patients with newly-diagnosed or recurrent glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT125.

High-Temporal-Resolution Kinetic Modeling of Lung Tumors with Dual-Blood Input Function Using Total-Body Dynamic PET.

The lungs are supplied by both the pulmonary arteries carrying deoxygenated blood originating from the right ventricle and the bronchial arteries carrying oxygenated blood downstream from the left ventricle. However, this effect of dual blood supply has never been investigated using PET, partially because the temporal resolution of conventional dynamic PET scans is limited. The advent of PET scanners with a long axial field of view, such as the uEXPLORER total-body PET/CT system, permits dynamic imaging with high temporal resolution (HTR). In this work, we modeled the dual-blood input function (DBIF) and studied its impact on the kinetic quantification of normal lung tissue and lung tumors using HTR dynamic PET imaging. Methods: Thirteen healthy subjects and 6 cancer subjects with lung tumors underwent a dynamic 18F-FDG scan with the uEXPLORER for 1 h. Data were reconstructed into dynamic frames of 1 s in the early phase. Regional time-activity curves of lung tissue and tumors were analyzed using a 2-tissue compartmental model with 3 different input functions: the right ventricle input function, left ventricle input function, and proposed DBIF, all with time delay and dispersion corrections. These models were compared for time-activity curve fitting quality using the corrected Akaike information criterion and for differentiating lung tumors from lung tissue using the Mann-Whitney U test. Voxelwise multiparametric images by the DBIF model were further generated to verify the regional kinetic analysis. Results: The effect of dual blood supply was pronounced in the high-temporal-resolution time-activity curves of lung tumors. The DBIF model achieved better time-activity curve fitting than the other 2 single-input models according to the corrected Akaike information criterion. The estimated fraction of left ventricle input was low in normal lung tissue of healthy subjects but much higher in lung tumors (∼0.04 vs. ∼0.3, P < 0.0003). The DBIF model also showed better robustness in the difference in 18F-FDG net influx rate [Formula: see text] and delivery rate [Formula: see text] between lung tumors and normal lung tissue. Multiparametric imaging with the DBIF model further confirmed the differences in tracer kinetics between normal lung tissue and lung tumors. Conclusion: The effect of dual blood supply in the lungs was demonstrated using HTR dynamic imaging and compartmental modeling with the proposed DBIF model. The effect was small in lung tissue but nonnegligible in lung tumors. HTR dynamic imaging with total-body PET can offer a sensitive tool for investigating lung diseases.

Analytical evaluation of circulating tumor DNA sequencing assays

In China, circulating tumor DNA analysis is widely used and numerous assays are available. Systematic evaluation to help users make informed selections is needed. Nine circulating tumor DNA assays, including one benchmark assay, were evaluated using 23 contrived reference samples. There were two sample types (cell-free DNA and plasma samples), three circulating tumor DNA inputs (low, < 20 ng; medium, 20–50 ng; high, > 50 ng), two variant allele frequency ranges (low, 0.1–0.5%; intermediate, 0.5–2.5%), and four variant types (single nucleotide, insertion/deletion, structural, and copy number). Sensitivity, specificity, reproducibility, and all processes from cell-free DNA extraction to bioinformatics analysis were assessed. The test assays were generally comparable or superior to the benchmark assay, demonstrating high analytical sensitivity. Variations in circulating tumor DNA extraction and quantification efficiency, sensitivity, and reproducibility were observed, particularly at lower inputs. These findings will guide circulating tumor DNA assay choice for research and clinical studies, allowing consideration of multiple technical parameters.

Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. Methods: This retrospective study consisted of 611 18F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on 18F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Results: Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively (P < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses (P < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. Conclusion: The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on 18F-FDG and PSMA PET/CT scans.

Determining the optimal pharmacokinetic modelling and simplified quantification method of [18F]AlF-P16-093 for patients with primary prostate cancer (PPCa).

This paper discusses the optimization of pharmacokinetic modelling and alternate simplified quantification method for [18F]AlF-P16-093, a novel tracer for in vivo imaging of prostate cancer. Dynamic PET/CT scans were conducted on eight primary prostate cancer patients, followed by a whole-body scan at 60 min post-injection. Time-activity curves (TACs) were obtained by drawing volumes of interest for primary prostatic and metastatic lesions. Optimal kinetic modelling involved evaluating three compartmental models (1T2K, 2T3K, and 2T4K) accounting for fractional blood volume (Vb). The simplified quantification method was then determined based on the correlation between the static uptake measure and total distribution volume (Vt) obtained from the optimal pharmacokinetic analysis. In total, 17 intraprostatic lesions, 10 lymph nodes, and 36 osseous metastases were evaluated. Visually, the contrast of the tumor increased and showed the steepest incline within the first few minutes, whereas background activity decreased over time. Full pharmacokinetic analysis revealed that a reversible two-compartmental (2T4K) model is the preferred kinetic model for the given tracer. The kinetic parameters K1, k3, Vb, and Vt were all significantly higher in lesions when compared with normal tissue (P < 0.01). Several simplified protocols were tested for approximating comprehensive dynamic quantification in tumors, with image-based SURmean (the ratio of tumor SUVmean to blood SUVmean) within the 28-34 min window found to be sufficient for approximating the total distribution Vt values (R2 = 0.949, P < 0.01). Both Vt and SURmean correlated significantly with the total serum prostate-specific antigen (tPSA) levels (P < 0.01). This study introduced an optimized pharmacokinetic modelling approach and a simplified acquisition method for [18F]AlF-P16-093, a novel PSMA-targeted radioligand, highlighting the feasibility of utilizing one static PET imaging (between 30 and 60 min) for the diagnosis of prostate cancer. Note that the image-derived input function in this study may not reflect the true corrected plasma input function, therefore the interpretation of the associated kinetic parameter estimates should be done with caution.

The value of color doppler ultrasonography combined with computed tomography angiography and magnetic resonance angiography in the preoperative quantification and classification of carotid body tumors: a retrospective analysis

BackgroundComputed tomography angiography (CTA) and magnetic resonance angiography (MRA) provide accurate vascular imaging information, but their use may be contraindicated. Color Doppler ultrasonography (CDU) provides simple, safe, noninvasive, and reproducible imaging. We therefore investigated the role of preoperative CDU combined with CTA and MRA in the quantification, typing, and diagnosis of carotid body tumors (CBTs).MethodsWe retrospectively analyzed patients with CBTs categorized into group A (type I [n = 1] and type II [n = 10]) or group B (type III [n = 56]) per the intraoperative Shamblin classification. CDU, CTA, and MRA characteristics of CBTs were observed, surgical results were correlated, and the diagnostic threshold of the CBT classification was calculated.ResultsCBTs were usually located at the common carotid artery bifurcation, encircling the carotid artery. An increased angle was found between the internal and external carotid arteries. On CDU, CBTs primarily presented as homogeneous hypoechoic masses with clear boundaries, rich flow signals, and a high-speed, low-resistance artery-like flow spectrum. CTA showed uniform or heterogeneous marked enhancement. MRA showed mixed T1 and slightly longer T2 signals and uniform or uneven obvious enhancement. With increases in the lesion size, amount of blood transfused, and operation time, the intraoperative classification level and possibility of skull-base invasion increased. When the maximum diameter of the lesion, the volume of the tumor, the distance between the upper margin of the tumor to the mastoid and the mandibular angle were 3.10 cm, 10.15 cm3, − 3.26 cm, and 0.57 cm, respectively, the largest Youden index was the best diagnostic boundary value for Shamblin type III tumors.ConclusionsCDU combined with CTA and MRA can accurately evaluate the size and classification of CBTs.

Magnetic resonance imaging-guided intracranial resection of glioblastoma tumors in patient-derived orthotopic xenografts leads to clinically relevant tumor recurrence

BackgroundPreclinical in vivo cancer models are essential tools for investigating tumor progression and response to treatment prior to clinical trials. Although treatment modalities are regularly assessed in mice upon tumor growth in vivo, surgical resection remains challenging, particularly in the orthotopic site. Here, we report a successful surgical resection of glioblastoma (GBM) in patient-derived orthotopic xenografts (PDOXs).MethodsWe derived a cohort of 46 GBM PDOX models that faithfully recapitulate human disease in mice. We assessed the detection and quantification of intracranial tumors using magnetic resonance imaging (MRI).To evaluate feasibility of surgical resection in PDOXs, we selected two models representing histopathological features of GBM tumors, including diffuse growth into the mouse brain. Surgical resection in the mouse brains was performed based on MRI-guided coordinates. Survival study followed by MRI and immunohistochemistry-based evaluation of recurrent tumors allowed for assessment of clinically relevant parameters.ResultsWe demonstrate the utility of MRI for the noninvasive assessment of in vivo tumor growth, preoperative programming of resection coordinates and follow-up of tumor recurrence. We report tumor detection by MRI in 90% of GBM PDOX models (36/40), of which 55% (22/40) can be reliably quantified during tumor growth. We show that a surgical resection protocol in mice carrying diffuse primary GBM tumors in the brain leads to clinically relevant outcomes. Similar to neurosurgery in patients, we achieved a near total to complete extent of tumor resection, and mice with resected tumors presented significantly increased survival. The remaining unresected GBM cells that invaded the normal mouse brain prior to surgery regrew tumors with similar histopathological features and tumor microenvironments to the primary tumors.ConclusionsOur data positions GBM PDOXs developed in mouse brains as a valuable preclinical model for conducting therapeutic studies that involve surgical tumor resection. The high detectability of tumors by MRI across a substantial number of PDOX models in mice will allow for scalability of our approach toward specific tumor types for efficacy studies in precision medicine-oriented approaches. Additionally, these models hold promise for the development of enhanced image-guided surgery protocols.

Spatial distributions of CD8 and Ki67 cells in the tumor microenvironment independently predict breast cancer-specific survival in patients with ER+HER2- and triple-negative breast carcinoma.

Breast cancer (BC) presents diverse malignancies with varying biological and clinical behaviors, driven by an interplay between cancer cells and tumor microenvironment. Deciphering these interactions is crucial for personalized diagnostics and treatment. This study explores the prognostic impact of tumor proliferation and immune response patterns, assessed by computational pathology indicators, on breast cancer-specific survival (BCSS) models in estrogen receptor-positive HER2-negative (ER+HER2-) and triple-negative BC (TNBC) patients. Whole-slide images of tumor surgical excision samples from 252 ER+HER2- patients and 63 TNBC patients stained for estrogen and progesterone receptors, Ki67, HER2, and CD8 were analyzed. Digital image analysis (DIA) was performed for tumor tissue segmentation and quantification of immunohistochemistry (IHC) markers; the DIA outputs were subsampled by hexagonal grids to assess the spatial distributions of Ki67-positive tumor cells and CD8-positive (CD8+) cell infiltrates, expressed as Ki67-entropy and CD8-immunogradient indicators, respectively. Prognostic models for BCSS were generated using multivariable Cox regression analysis, integrating clinicopathological and computational IHC indicators. In the ER+HER2- BC, multivariable Cox regression revealed that high CD8+ density within the tumor interface zone (IZ) (HR: 0.26, p = 0.0056), low immunodrop indicator of CD8+ density (HR: 2.93, p = 0.0051), and low Ki67-entropy (HR: 5.95, p = 0.0.0061) were independent predictors of better BCSS, while lymph node involvement predicted worse BCSS (HR: 3.30, p = 0.0013). In TNBC, increased CD8+ density in the IZ stroma (HR: 0.19, p = 0.0119) and Ki67-entropy (HR: 3.31, p = 0.0250) were independent predictors of worse BCSS. Combining these independent indicators enhanced prognostic stratification in both BC subtypes. Computational biomarkers, representing spatial properties of the tumor proliferation and immune cell infiltrates, provided independent prognostic information beyond conventional IHC markers in BC. Integrating Ki67-entropy and CD8-immunogradient indicators into prognostic models can improve patient stratification with regard to BCSS.

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma.

Patients with lymphoma receive multiple positron emission tomography/computed tomography (PET/CT) exams for monitoring of the therapeutic response. With PET imaging, a reduced level of injected fluorine-18 fluorodeoxyglucose ([18F]FDG) activity can be administered while maintaining the image quality. In this study, we investigated the efficacy of applying a deep learning (DL) denoising-technique on image quality and the quantification of metabolic parameters and Deauville score (DS) of a low [18F]FDG dose PET in patients with lymphoma. This study retrospectively enrolled 62 patients who underwent [18F]FDG PET scans. The low-dose (LD) data were simulated by taking a 50% duration of routine-dose (RD) PET list-mode data in the reconstruction, and a U-Net-based denoising neural network was applied to improve the images of LD PET. The visual image quality score (1 = undiagnostic, 5 = excellent) and DS were assessed in all patients by nuclear radiologists. The maximum, mean, and standard deviation (SD) of the standardized uptake value (SUV) in the liver and mediastinum were measured. In addition, lesions in some patients were segmented using a fixed threshold of 2.5, and their SUV, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) were measured. The correlation coefficient and limits of agreement between the RD and LD group were analyzed. The visual image quality of the LD group was improved compared with the RD group. The DS was similar between the RD and LD group, and the negative (DS 1-3) and positive (DS 4-5) results remained unchanged. The correlation coefficients of SUV in the liver, mediastinum, and lesions were all >0.85. The mean differences of SUVmax and SUVmean between the RD and LD groups, respectively, were 0.22 [95% confidence interval (CI): -0.19 to 0.64] and 0.02 (95% CI: -0.17 to 0.20) in the liver, 0.13 (95% CI: -0.17 to 0.42) and 0.02 (95% CI: -0.12 to 0.16) in the mediastinum, and -0.75 (95% CI: -3.42 to 1.91), and -0.13 (95% CI: -0.57 to 0.31) in lesions. The mean differences in MTV and TLG were 0.85 (95% CI: -2.27 to 3.98) and 4.06 (95% CI: -20.53 to 28.64) between the RD and LD groups. The DL denoising technique enables accurate tumor assessment and quantification with LD [18F]FDG PET imaging in patients with lymphoma.

Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging.

Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology.