Imaging biomarkers for evaluating tumor response: RECIST and beyond

NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC

BACKGROUND

Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy remains a critical unmet need in resectable non-small-cell lung cancer (NSCLC). Conventional size-based imaging criteria offer limited reliability, while biopsy confirmation is available only post-surgery.

METHODS

We retrospectively assembled 509 consecutive NSCLC cases from four Chinese thoracic-oncology centers (March 2018 to March 2023) and prospectively enrolled 50 additional patients. Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. Model performance was measured by area under the receiver-operating-characteristic curve (AUC) and compared with nine board-certified radiologists.

RESULTS

In an external validation set (n=59), NeoPred achieved an AUC of 0.772 (95% CI: 0.650 to 0.895), sensitivity 0.591, specificity 0.733, and accuracy 0.627; incorporating clinical data increased the AUC to 0.787. In a prospective cohort (n=50), NeoPred reached an AUC of 0.760 (95% CI: 0.628 to 0.891), surpassing the experts' mean AUC of 0.720 (95% CI: 0.574 to 0.865). Model assistance raised the pooled expert AUC to 0.829 (95% CI: 0.707 to 0.951) and accuracy to 0.820. Marked performance persisted within radiological stable-disease subgroups (external AUC 0.742, 95% CI: 0.468 to 1.000; prospective AUC 0.833, 95% CI: 0.497 to 1.000).

CONCLUSIONS

Combining dual-phase CT and clinical variables, NeoPred reliably and non-invasively predicts pathological response to neoadjuvant chemo-immunotherapy in NSCLC, outperforms unaided expert assessment, and significantly enhances radiologist performance. Further multinational trials are needed to confirm generalizability and support surgical decision-making.

The Desmoid Dilemma: Challenges and Opportunities in Assessing Tumor Burden and Therapeutic Response

Desmoid tumors are rare, locally invasive soft-tissue tumors with unpredictable clinical behavior. Imaging plays a crucial role in their diagnosis, measurement of disease burden, and assessment of treatment response. However, desmoid tumors' unique imaging features present challenges to conventional imaging metrics. The heterogeneous nature of these tumors, with a variable composition (fibrous, myxoid, or cellular), complicates accurate delineation of tumor boundaries and volumetric assessment. Furthermore, desmoid tumors can demonstrate prolonged stability or spontaneous regression, and biologic quiescence is often manifested by collagenization rather than bulk size reduction, making traditional size-based response criteria, such as Response Evaluation Criteria in Solid Tumors (RECIST), suboptimal. To overcome these limitations, advanced imaging techniques offer promising opportunities. Functional and parametric imaging methods, such as diffusion-weighted MRI, dynamic contrast-enhanced MRI, and T2 relaxometry, can provide insights into tumor cellularity and maturation. Radiomics and artificial intelligence approaches may enhance quantitative analysis by extracting and correlating complex imaging features with biological behavior. Moreover, imaging biomarkers could facilitate earlier detection of treatment efficacy or resistance, enabling tailored therapy. By integrating advanced imaging into clinical practice, it may be possible to refine the evaluation of disease burden and treatment response, ultimately improving the management and outcomes of patients with desmoid tumors.

Radiomic-based models are able to predict the pathologic response to different neoadjuvant chemotherapy regimens in patients with gastric and gastroesophageal cancer: a cohort study

BACKGROUND

There is a clinical need to identify early predictors for response to neoadjuvant chemotherapy (NAC) in patients with gastric and gastroesophageal junction cancer (GC and GEJC). Radiomics involves extracting quantitative features from medical images. This study aimed to apply radiomics to build prediction models for the response to NAC.

METHODS

All consecutive patients with non-metastatic GC and GEJC undergoing NAC and surgical resection in an Italian high-volume referral center between 2005 and 2021 were considered eligible. In patients selected, the CT scans performed upon staging were reviewed to segment the tumor and extract radiomic features using MODDICOM. The primary endpoint was to develop and validate radiomic-based predictive models to identify major responders (MR: tumor regression grade TRG 1-2) and non-responders (NR: TRG 4-5) to NAC. Following an initial feature selection, radiomic and combined radiomic-clinicopathologic prediction models were built for the MR or NR status based on logistic regressions. Internal validation was performed for each model. Radiomic models (in the entire case series and according to NAC regimens) were evaluated using the receiver operating characteristic area under the curve (AUC), sensitivity, and negative predictive value (NPV).

RESULTS

The study included 77 patients undergoing NAC and subsequent tumor resection. The MR prediction model after all types of NAC (AUC of 0.876, CI 95% 0.786 - 0.966, sensitivity 83%, and NPV 96%) was based on a statistical feature. The models predicting NR among patients undergoing epirubicin with cisplatin and fluorouracil (ECF), epirubicin with oxaliplatin and capecitabin (EOX), or fluorouracil with oxaliplatin and docetaxel (FLOT) (AUC 0.760, CI 95% 0.639-0.882), oxaliplatin-based chemotherapy (AUC 0.810, CI 95% 0.692-0.928), and FLOT (AUC 0.907, CI 95% 0.818 - 0.995) were based on statistical, morphological and textural features.

CONCLUSIONS

The developed radiomic models resulted promising in predicting the response to different neoadjuvant chemotherapy strategies. Once further implemented on larger datasets, they could be valuable and cost-effective instruments to target multimodal treatment in patients with GC.

Beyond Conventional CT: Role of Dual-Energy CT in Monitoring Response to Therapy in Abdominal Malignancies

In the era of precision medicine, imaging plays a critical role in evaluating treatment response to various oncologic therapies. For decades, conventional morphologic assessments using cross-sectional imaging have been the standard for monitoring the effectiveness of systemic and locoregional therapies in patients with cancer. However, the development of new functional imaging tools has widened the scope of imaging from mere response assessment to patient selection and outcome prediction. Dual-energy CT (DECT), known for its superior material differentiation capabilities, shows promise in enhancing treatment response evaluation. DECT-based iodine quantification methods are increasingly being investigated as surrogates for assessing tumor vascularity and physiology, which is particularly important in patients undergoing emerging targeted therapies. The purpose of this review article is to discuss the current and emerging role of DECT in assessing treatment response in patients with malignant abdominal tumors. Keywords: CT-Dual Energy, Transcatheter Tumor Therapy, Tumor Response, Iodine Uptake, Therapeutic Response © RSNA, 2025.

Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning

BACKGROUND

Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par.

PURPOSE

To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI.

MATERIAL AND METHODS

The interactive method requires the user to click six points near the tumor's extreme boundaries in the image. These six points are transformed into a distance map and serve, with the image, as input for a convolutional neural network. A multi-center public dataset with 514 patients and nine STT phenotypes in seven anatomical locations, with CT or T1-weighted MRI, was used for training and internal validation. For external validation, another public dataset was employed, which included five unseen STT phenotypes in extremities on CT, T1-weighted MRI, and T2-weighted fat-saturated (FS) MRI.

RESULTS

Internal validation resulted in a dice similarity coefficient (DSC) of 0.85 ± 0.11 (mean ± standard deviation) for CT and 0.84 ± 0.12 for T1-weighted MRI. External validation resulted in DSCs of 0.81 ± 0.08 for CT, 0.84 ± 0.09 for T1-weighted MRI, and 0.88 ± 0.08 for T2-weighted FS MRI. Volumetric measurements showed consistent replication with low error internally (volume: 1 ± 28 mm3, r = 0.99; diameter: - 6 ± 14 mm, r = 0.90) and externally (volume: - 7 ± 23 mm3, r = 0.96; diameter: - 3 ± 6 mm, r = 0.99). Interactive segmentation time was considerably shorter (CT: 364 s, T1-weighted MRI: 258s) than manual segmentation (CT: 1639s, T1-weighted MRI: 1895s).

CONCLUSION

The minimally interactive segmentation method effectively segments STT phenotypes on CT and MRI, with robust generalization to unseen phenotypes and imaging modalities.

KEY POINTS

Question Can this deep learning-based method segment soft-tissue tumors faster than can be done manually and more accurately than other automatic methods? Findings The minimally interactive segmentation method achieved accurate segmentation results in internal and external validation, and generalized well across soft-tissue tumor phenotypes and imaging modalities. Clinical relevance This minimally interactive deep learning-based segmentation method could reduce the burden of manual segmentation, facilitate the integration of imaging-based biomarkers (e.g., radiomics) into clinical practice, and provide a fast, semi-automatic solution for volume and diameter measurements (e.g., RECIST).

ESR Essentials: assessing the radiological response of liver metastases to systemic therapy-practice recommendations by the European Society of Gastrointestinal and Abdominal Radiology

The liver is a common site for metastatic spread, especially in advanced colorectal, breast, and pancreatic cancers. Imaging evaluation of liver metastases after systemic treatments like chemotherapy, targeted therapy, or immunotherapy is essential to distinguish treatment response from disease progression. The widely used response evaluation criteria in solid tumours (RECIST 1.1) focus on lesion size changes to evaluate treatment response. However, newer therapies, mainly targeted therapy and immunotherapy, often induce changes beyond size reduction, such as tumour necrosis, fibrosis, cystic transformation, calcifications, and modifications at the liver-tumour interface. These morphological and enhancement changes can be evaluated on CT and MRI and may better reflect the biological response in specific clinical settings. Overall, RECIST 1.1 criteria are recommended for assessing the radiological response of liver metastases after systemic treatment. The use of alternative radiological criteria validated on CT (such as Chun or Choi criteria) is recommended in specific clinical settings (e.g. metastatic colorectal cancer or metastatic gastrointestinal stromal tumours). Additionally, CT and MR modifications that reflect fibrosis, necrosis, calcifications, and haemorrhage can serve as ancillary indicators of tumoural response. These alternative criteria and radiological findings should be systematically assessed, particularly in liver metastases with minimal size changes, to better identify responders. KEY POINTS: RECIST 1.1 is the standard for evaluating tumour response in solid tumours and is recommended for the assessment of liver metastases after systemic therapy. CT attenuation, enhancement, and liver/tumour interface may correlate better with tumoural response compared to size reduction. CT and MR changes suggesting necrosis, fibrosis, calcifications, and haemorrhage can be used as additional indicators of tumoural response.

A novel pN stage prediction model for resectable rectal adenocarcinoma based on preoperative MRI features and multiregional apparent diffusion coefficients

OBJECTIVE

To develop and validate a novel model based on preoperative MRI features and multiregional apparent diffusion coefficients (ADCs) to improve the prediction of pN stage in resectable rectal adenocarcinoma (RA).

METHODS

Two hundred fifty-four consecutive patients (median age [interquartile range], 67 [56-74] years; 156 males) with resectable RA were retrospectively collected at two medical centers from January 2017 to December 2023 and were divided into the training (n = 139), internal validation (n = 60), and external validation (n = 55) sets. All patients underwent preoperative MRI scans. Univariate and multivariate logistic regression analyses were conducted on the MRI features and multiregional (RA, peritumoral tissue, and tumor-adjacent rectal wall) ADCs to construct a nomogram model for preoperative predicting pN stage in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive performance of the nomogram model vs the conventional MRI-assessed N (mriN) stage. The ROC curves were compared using the DeLong test.

RESULTS

The predictors incorporated in the nomogram model comprised gross tumor volume, categories of short diameter of maximum node, extramural vascular invasion, mesorectal fascia involvement, and ADCs of RA and peritumoral tissue. This model yielded a better prediction of the pN stage compared to the mriN stage in training (AUC, 0.848 vs 0.672; p < 0.001), internal validation (AUC, 0.843 vs 0.699; p = 0.008), and external validation (AUC, 0.857 vs 0.723; p = 0.01) sets.

CONCLUSION

This novel model based on the preoperative MRI features and multiregional ADCs can improve the prediction of the pN stage in RA.

KEY POINTS

Question Accurate preoperative assessment of the pN stage is important for determining an appropriate therapeutic strategy in rectal cancer, but the conventional mriN stage has low sensitivity. Findings Utilization of certain MRI features and multiregional ADCs improves preoperative assessment of the pN stage in RA when compared with conventional MRI assessment. Clinical relevance The novel model, based on preoperative MRI features and multiregional ADC values, can improve the prediction of the pN stage compared to the mriN stage in RA. The combination of this model with the mriN stage helps personalize treatment plans to improve patient prognosis.

Variability of HCC Tumor Diameter and Density Measurements on Dynamic Contrast-Enhanced Computed Tomography

PURPOSE

In cancers imaged using contrast-enhanced protocols, such as hepatocellular carcinoma (HCC), formal guidelines rely on measurements of lesion size (in mm) and radiographic density (in Hounsfield units [HU]) to evaluate response to treatment. However, the variability of these measurements across different contrast enhancement phases remains poorly understood. This limits the ability of clinicians to discern whether measurement changes are accurate.

METHODS

In this study, we investigated the variability of maximal lesion diameter and mean lesion density of HCC lesions on CT scans across four different contrast enhancement phases: non-contrast-enhanced phase (NCE), early arterial phase (E-AP), late arterial phase (L-AP), and portal venous phase (PVP). HCC lesions were independently segmented by two expert radiologists. For each pair of a lesion's scan timepoints, one was selected randomly as the baseline measurement and the other as the repeat measurement. Both absolute and relative differences in measurements were calculated, as were the coefficients of variance (CVs). Analysis was further stratified by both contrast enhancement phase and lesion diameter.

RESULTS

Lesion diameter was found to have a CV of 5.11% (95% CI: 4.20-6.01%). About a fifth of the measurement's relative changes were greater than 10%. Although there was no significant difference in diameter measurements across different phases, there was a significant negative correlation (R = -0.303, p-value = 0.030) between lesion diameter and percent difference in diameter measurement. Lesion density measurements varied significantly across all phases, with the greatest relative difference of 47% in the late arterial phase and a CV of 22.84% (21.48-24.20%). The overall CV for lesion density measurements was 26.19% (24.66-27.72%).

CONCLUSIONS

Changes in tumor diameter measurements within 10% may simply be due to variability, and lesion density is highly sensitive to contrast timing. This highlights the importance of paying attention to these two variables when evaluating tumor response in both clinical trials and practice.

Comparison of RECIST 1.1, mRECIST and PERCIST for assessment of peptide receptor radionuclide therapy treatment response in metastatic neuroendocrine tumors

PURPOSE

To compare RECIST 1.1, modified RECIST (mRECIST) and PERCIST for assessment of Peptide Receptor Radionuclide Therapy (PRRT) treatment response in metastatic neuroendocrine tumors.

MATERIALS

In this IRB-approved, HIPAA compliant retrospective study, patients treated with PRRT between July 2019 and Dec 2022 were identified. Inclusion criteria were presence of at least one pre-and one post-treatment imaging (CT, MRI, Ga 68 or Cu64 DOTATATE PET/CT) within one year of the start and end of PRRT respectively. The imaging was reviewed independently by two radiologists using RECIST 1.1, modified RECIST (mRECIST) and PERCIST criteria. Response of first post treatment scan and presence of disease progression during follow-up were recorded along with the date of best response and disease progression. Statistical analysis was performed to determine inter-reader agreement and agreement between the various response criteria using kappa statistics.

RESULTS

Best response by RECIST 1.1 was recorded in 26 patients (PR-7, SD- 13, PD- 6), by mRECIST in 22 patients (PR-7, SD- 10, PD- 5), by PERCIST in 14 patients (PR-4, SD- 3, PD- 7). Inter-reader agreement was highest for PERCIST (weighted kappa 0.921, standard error 0.078 95% CI 0.769 to 1.000) followed by RECIST 1.1 (weighted kappa 0.897, standard error 0.071 95% CI 0.758 to 1.000) and mRECIST (weighted kappa 0.883, standard error 0.079 95% CI 0.727 to 1.000).

Synthetic MRI Combined With Clinicopathological Characteristics for Pretreatment Prediction of Chemoradiotherapy Response in Advanced Nasopharyngeal Carcinoma

OBJECTIVE

To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC).

MATERIALS AND METHODS

Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiver-operating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test.

RESULTS

The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein-Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively.

CONCLUSION

SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Comparison of apparent diffusion coefficients of resectable mid‑high rectal adenocarcinoma and distal paracancerous tissue

Paracancerous tissues actively communicate with the tumor and undergo molecular alterations associated with tumorigenesis. Apparent diffusion coefficient (ADC) can help distinguish between rectal adenocarcinoma (RA), tumor-adjacent and tumor-distant tissues. Preoperative determining optimal distal resection margin (DRM) is crucial for formulating surgical options. The present study aimed to assess ADC differences between RA and 1 cm-layer distal paracancerous tissues, providing a potential reference basis for preoperatively determining optimal DRM. A total of 110 consecutive patients with mid-high RA undergoing preoperative diffusion-weighted imaging were included. ADCs of RA and distal paracancerous tissues located ~1, 2 and 3 cm from the tumor margin (defined as D1, D2 and D3, respectively) were measured using five b-value pairs (0 and 50; 0 and 100; 0 and 800; 0 and 1,000; and 0 and 1,500 sec/mm2). Differences in ADCs between RA, D1, D2 and D3 were compared using the Friedman test with a post hoc Bonferroni correction. Variables that demonstrated statistical differences in multiple pairwise comparisons underwent receiver operating characteristic (ROC) analysis to assess diagnostic performance of ADCs in distinguishing between tissues. ADC at all b-value pairs demonstrated satisfactory performance in distinguishing RA from D1, D2 and D3 [areas under the ROC curves (AUCs), 0.838 to 0.996)]. When the maximum b-value was ≥800 sec/mm2, the ADC of D1 was significantly lower compared with those of D2 and D3 (P<0.001). ADC exhibited an optimal performance in differentiating D1 from D2 at b-values of 0 and 800 sec/mm2, and D1 from D3 at b-values of 0 and 1,000 sec/mm2 (AUCs: 0.652 and 0.692, respectively). However, ADCs of D2 and D3 demonstrated no differences at all b-value pairs (all P>0.05). In conclusion, ADC may distinguish RA from D1, D2 and D3, and D1 from D2/D3, but cannot distinguish between D2 and D3.

Reduction of Streak Artifacts in the Superior Vena Cava for Better Visualization of Mediastinal Structures Through Virtual Monoenergetic Reconstructions Using a Photon-counting Detector Computed Tomography

PURPOSE

Computed tomography (CT) is crucial in oncologic imaging for precise diagnosis and staging. Beam-hardening artifacts from contrast media in the superior vena cava can degrade image quality and obscure adjacent structures, complicating lymph node assessment. This study examines the use of virtual monoenergetic reconstruction with photon-counting detector CT (photon-counting CT) to mitigate these artifacts.

MATERIALS AND METHODS

The retrospective study included 50 patients who underwent thoracoabdominal scans. Virtual monoenergetic reconstructions at nine keV levels (60 to 140 keV) were analyzed for Hounsfield Unit (HU) stability, image noise, and artifact index in various regions of interest (ROIs): mediastinal adipose tissue (ROI 1 to 3) and vascular stations (ROI 4 to 6) were compared with reference tissue (ROI 7 to 8). The diagnostic image quality of the keV levels was assessed using a 5-point Likert Scale.

RESULTS

Lower keV values (60 to 80) exhibited higher image noise and lower HU stability in mediastinal adipose tissue compared with higher energies, with optimal noise reduction observed at 130 keV (ROI 1 to 3). HU stability in vascular structures (ROI 4 to 6) significantly improved above 80 keV, with the best performance at 140 keV. Artifact levels decreased progressively from 60 to 140 keV. Visually, keV levels of 110 keV (96% Likert ≥4) and 120 keV (60% Likert 4) were rated most diagnostically valuable, consistent with technical findings.

CONCLUSION

Virtual monoenergetic reconstructions with photon-counting CT effectively reduce beam-hardening artifacts near the superior vena cava, enhancing the visualization of lymph nodes and adjacent structures. This technology advances oncologic imaging by improving diagnostic accuracy in areas previously affected by artifact-related image degradation.

Neoadjuvant pyrotinib and trastuzumab in HER2-positive breast cancer with no early response (NeoPaTHer): efficacy, safety and biomarker analysis of a prospective, multicentre, response-adapted study

The potential benefits of pyrotinib for patients with trastuzumab-insensitive, HER2-positive early-stage breast cancer remain unclear. This prospective, multicentre, response-adapted study evaluated the efficacy and safety of adding pyrotinib to the neoadjuvant treatment of HER2-positive breast cancer patients with a poor response to initial docetaxel plus carboplatin and trastuzumab (TCbH). Early response was assessed using magnetic resonance imaging (MRI) after two cycles of treatment. Patients showing poor response, as defined by RECIST 1.1, could opt to receive additional pyrotinib or continue standard therapy. The primary endpoint was the total pathological complete response (tpCR; ypT0/isN0) rate. Of the 129 patients enroled, 62 (48.1%) were identified as MRI-responders (cohort A), 26 non-responders continued with four more cycles of TCbH (cohort B), and 41 non-responders received additional pyrotinib (cohort C). The tpCR rate was 30.6% (95% CI: 20.6-43.0%) in cohort A, 15.4% (95% CI: 6.2-33.5%) in cohort B, and 29.3% (95% CI: 17.6-44.5%) in cohort C. Multivariable logistic regression analyses demonstrated comparable odds of achieving tpCR between cohorts A and C (odds ratio = 1.04, 95% CI: 0.40-2.70). No new adverse events were observed with the addition of pyrotinib. Patients with co-mutations of TP53 and PIK3CA exhibited lower rates of early partial response compared to those without or with a single gene mutation (36.0% vs. 60.0%, P = 0.08). These findings suggest that adding pyrotinib may benefit patients who do not respond to neoadjuvant trastuzumab plus chemotherapy. Further investigation is warranted to identify biomarkers predicting patients' benefit from the addition of pyrotinib.

CT-Derived Quantitative Image Features Predict Neoadjuvant Treatment Response in Adenocarcinoma of the Gastroesophageal Junction with High Accuracy

BACKGROUND

The purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ).

METHODS

A total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection. Histopathological parameters and tumor regression grading according to Becker et al. were collected in 93 patients. Line profiles of the primary tumor area in baseline and preoperative CE-CT were generated using ImageJ. Maximum tumor density and tumor-to-wall density delta were calculated and correlated with the histopathological tumor response. In addition, tumor response was assessed according to standard RECIST measurements in all patients and by endoscopy in 72 patients.

RESULTS

Baseline and change in baseline to preoperative CE-CT parameters showed no significant differences between responders (Becker grade 1a, 1b) and non-responders (Becker grade 2, 3). After neoadjuvant therapy, responders and non-responders showed significant differences in maximum density and tumor-to-wall density delta values. Line profile measurements showed excellent inter-rater agreement. In comparison, neither RECIST nor endoscopy showed significant differences between these groups.

CONCLUSIONS

Posttreatment CE-CT can predict histopathological therapy response to neoadjuvant treatment in adenocarcinoma of GEJ patients with high accuracy and thus may improve patient management.

Remodeling and Characterization Analysis of Corticospinal Tract in Patients with Intracerebral Hemorrhage in the Basal Ganglia

To investigate corticospinal tract (CST) injury and remodeling in patients with basal ganglia intracerebral hemorrhage (ICH) and explore the characterization capabilities of the corresponding parameters. In this prospective study, baseline, scale, and diffusion-weighted imaging (DWI) data were collected from patient cohorts. Participants were stratified into favorable (0-3 points) and unfavorable (4-6 points) prognosis groups, based on Modified Rankin Scale (mRS) after 3-6 months. The analysis of DWI data was conducted employing FSL and DSI Studio software to compare CST injury between the prognosis groups and CST remodeling features. A partial correlation model was deployed to elucidate the characterization capability of CST-related parameters. Additionally, logistic regression analysis was applied to identify factors significantly influencing prognosis. A total of 65 patients were enrolled with a mean age of 53.52 years and a median hematoma volume of 23.60 ml. The 44 patients were classified within the favorable prognosis group, demonstrating a statistically significant difference in their lower mean age (P = 0.002). Additionally, 10 patients underwent DWI review with a mean age of 50.30 years and a median hematoma volume of 18.56 ml. The investigation uncovered evidence of CST damage versus remodeling at the group level, respectively, with statistical significance (FDR-corrected P < 0.05, 10,000 permutations). The fractional anisotropy (FA) ratio in the internal capsule region exhibited moderate correlation with motor function (r = 0.507, P < 0.001) and the 3- to 6-month mRS scores (r =  - 0.318, P < 0.013). Furthermore, binary logistic regression analysis identified the FA rate in the internal capsule as a significant influencing factor of prognosis (odds ratio = 1.027, 95% confidence interval = 1.003-1.052, P = 0.025). Basal ganglia ICH can coincide with injury to the CST, which could undergo repair over time. Additionally, the FA ratio of the internal capsule is a potential biomarker to characterize residual motor function and provide prognostic information.

The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer

Currently, the second most commonly diagnosed cancer in the world is lung cancer, and 85% of cases are non-small cell lung cancer (NSCLC). With growing knowledge of oncogene drivers and cancer immunology, several novel therapeutics have emerged to improve the prognostic outcomes of NSCLC. However, treatment outcomes remain diverse, and an accurate tool to achieve precision medicine is an unmet need. Radiomics, a method of extracting medical imaging features, is promising for precision medicine. Among all radiomic tools, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)-based radiomics provides distinct information on glycolytic activity and heterogeneity. In this review, we collected relevant literature from PubMed and summarized the various applications of 18F-FDG PET-derived radiomics in improving the detection of metastasis, subtyping histopathologies, characterizing driver mutations, assessing treatment response, and evaluating survival outcomes of NSCLC. Furthermore, we reviewed the values of 18F-FDG PET-based deep learning. Finally, several challenges and caveats exist in the implementation of 18F-FDG PET-based radiomics for NSCLC. Implementing 18F-FDG PET-based radiomics in clinical practice is necessary to ensure reproducibility. Moreover, basic studies elucidating the underlying biological significance of 18F-FDG PET-based radiomics are lacking. Current inadequacies hamper immediate clinical adoption; however, radiomic studies are progressively addressing these issues. 18F-FDG PET-based radiomics remains an invaluable and indispensable aspect of precision medicine for NSCLC.

Integrated modeling of biomarkers, survival and safety in clinical oncology drug development

Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs.

Advancements in lung cancer: a comprehensive perspective on diagnosis, staging, therapy and follow-up from the SAKK Working Group on Imaging in Diagnosis and Therapy Monitoring

In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics. Treatment aspects, such as preoperative assessment and the implementation of immune response evaluation criteria in solid tumours (iRECIST), have also seen advancements. For those undergoing curative treatment for lung cancer, guidelines propose follow-up with computed tomography (CT) scans within a specific timeframe. However, discrepancies exist in published guidelines, and there is a lack of universally accepted recommendations for follow-up procedures. This white paper aims to provide a certain standard regarding the use of imaging on the diagnosis, staging, treatment and follow-up of patients with lung cancer.

Dedicated software to harmonize the follow-up of oncological patients

Objective

To test and evaluate a sofware dedicated to the follow-up of oncological CT scans for potential use in the Radiology department.

Materials and methods

In this retrospective study, 37 oncological patients with baseline and follow-up CT scans were reinterpreted using a dedicated software. Baseline CT scans were chosen from the imaging reports available in our PACS (picture archiving and communicatin systems). Follow-up interpretations were independently assessed with the software. We evaluated the target lesion sums and the tumor response based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors).

Results

There was no significant difference in the target lesion sums and the tumor response assessments between the PACS data and the imaging software. There was no over or underestimation of the disease with the software. There was a sigificant deviation (progression versus stability) in three cases. For two patients, this difference was related to the evaluation of the response of non-target lesions. The difference in the third patient was due to comparison with a previous CT scan than to the baseline exam. There was a miscalculation in 13 % of the reports and in 28 % of the cases the examination was compared to the previous CT scan. Finally, the tumor response was not detailed in 43 % of the follow-up reports.

Conclusion

The use of dedicated oncology monitoring software could help in reducing intepretation time and in limiting human errors.

Feasibility of an artificial intelligence system for tumor response evaluation

PURPOSE

The objective of this study was to evaluate the feasibility of using Artificial Intelligence (AI) to measure the long-diameter of tumors for evaluating treatment response.

METHODS

Our study included 48 patients with lung-specific target lesions and conducted 277 measurements. The radiologists recorded the long-diameter in axial imaging plane of the target lesions for each measurement. Meanwhile, AI software was utilized to measure the long-diameter in both the axial imaging plane and in three dimensions (3D). Statistical analyses including the Bland-Altman plot, Spearman correlation analysis, and paired t-test to ascertain the accuracy and reliability of our findings.

RESULTS

The Bland-Altman plot showed that the AI measurements had a bias of -0.28 mm and had limits of agreement ranging from - 13.78 to 13.22 mm (P = 0.497), indicating agreement with the manual measurements. However, there was no agreement between the 3D measurements and the manual measurements, with P < 0.001. The paired t-test revealed no statistically significant difference between the manual measurements and AI measurements (P = 0.497), whereas a statistically significant difference was observed between the manual measurements and 3D measurements (P < 0.001).

CONCLUSIONS

The application of AI in measuring the long-diameter of tumors had significantly improved efficiency and reduced the incidence of subjective measurement errors. This advancement facilitated more convenient and accurate tumor response evaluation.

Optimal cut-off value for identifying objective response in patients with nasopharyngeal carcinoma after induction chemotherapy

BACKGROUND

We aimed to establish the most suitable threshold for objective response (OR) in the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 in patients with nasopharyngeal carcinoma (NPC).

METHODS

According to RECIST 1.1, we retrospectively evaluated MR images of NPC lesions in patients before and after induction chemotherapy (IC). Restricted cubic spline and maximally selected rank statistics were used to determine the cut-off value. Survival rates and differences between groups were compared with Kaplan-Meier curves and log-rank tests.

RESULTS

Of 1126 patients, 365 cases who received IC treatment were suitable for RECIST 1.1 evaluation. The 20% cut-off value maximized between-group differences according to maximally selected rank statistics. No difference in distant metastasis-free survival between OR and non-response groups was shown using the primary threshold of OR (30%), while it differed when 20% was employed.

CONCLUSIONS

With an optimal cut-off value of 20%, RECIST may assist clinicians to accurately evaluate disease response in NPC patients.

Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy

OBJECTIVES

This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC).

MATERIALS AND METHODS

A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods.

RESULTS

The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients.

CONCLUSION

The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT.

CLINICAL RELEVANCE STATEMENT

The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment.

KEY POINTS

• The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.

Navigating the landscape of theranostics in nuclear medicine: current practice and future prospects

Theranostics refers to the combination of diagnostic biomarkers with therapeutic agents that share a specific target expressed by diseased cells and tissues. Nuclear medicine is an exciting component explored for its applicability in theranostic concepts in clinical and research investigations. Nuclear theranostics is based on the employment of radioactive compounds delivering ionizing radiation to diagnose and manage certain diseases employing binding with specifically expressed targets. In the realm of personalized medicine, nuclear theranostics stands as a beacon of potential, potentially revolutionizing disease management. Studies exploring the theranostic profile of radioactive compounds have been presented in this review along with a detailed explanation of radioactive compounds and their theranostic applicability in several diseases. It furnishes insights into their applicability across diverse diseases, elucidating the intricate interplay between these compounds and disease pathologies. Light is shed on the important milestones of nuclear theranostics beginning with radioiodine therapy in thyroid carcinomas, MIBG labelled with iodine in neuroblastoma, and several others. Our perspectives have been put forth regarding the most important theranostic agents along with emerging trends and prospects.

The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management.

RAC1 inhibition ameliorates IBSP-induced bone metastasis in lung adenocarcinoma

Macrophage-to-osteoclast differentiation (osteoclastogenesis) plays an essential role in tumor osteolytic bone metastasis (BM), while its specific mechanisms remain largely uncertain in lung adenocarcinoma BM. In this study, we demonstrate that integrin-binding sialoprotein (IBSP), which is highly expressed in the cancer cells from bone metastatic and primary lesions of patients with lung adenocarcinoma, can facilitate BM and directly promote macrophage-to-osteoclast differentiation independent of RANKL/M-CSF. In vivo results further suggest that osteolytic BM in lung cancer specifically relies on IBSP-induced macrophage-to-osteoclast differentiation. Mechanistically, IBSP regulates the Rac family small GTPase 1 (Rac1)-NFAT signaling pathway and mediates the forward shift of macrophage-to-osteoclast differentiation, thereby leading to early osteolysis. Moreover, inhibition of Rac1 by EHT-1864 or azathioprine in mice models can remarkably alleviate IBSP-induced BM of lung cancer. Overall, our study suggests that tumor-secreted IBSP promotes BM by inducing macrophage-to-osteoclast differentiation, with potential as an early diagnostic maker for BM, and Rac1 can be the therapeutic target for IBSP-promoted BM in lung cancer.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer

OBJECTIVE

Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features.

METHODS

This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS).

RESULTS

Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival.

CONCLUSION

Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early.

CRITICAL RELEVANCE STATEMENT

Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging.

KEY POINTS

Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

RNA biomarkers in cancer therapeutics: The promise of personalized oncology

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

Multiparametric quantification of T1 and T2 relaxation time of bone metastasis in comparison with red or fatty bone marrow using magnetic resonance fingerprinting

OBJECTIVES

To assess the T1 and T2 values of bone marrow lesions in spine and pelvis derived from magnetic resonance fingerprinting (MRF) and to evaluate the differences in values among bone metastasis, red marrow and fatty marrow.

METHODS

Sixty patients who underwent lumbar spine and pelvic MRI with magnetic resonance fingerprinting were retrospectively included. Among eligible patients, those with bone metastasis, benign red marrow deposition and normal fatty marrow were identified. Two radiologists independently measured the T1 and T2 values from metastatic bone lesions, fatty marrow, and red marrow deposition on three-dimensional-magnetic resonance fingerprinting. Intergroup comparison and interobserver agreement were analyzed.

RESULTS

T1 relaxation time was significantly higher in osteoblastic metastasis than in red marrow (1674.6 ± 436.3 vs 858.7 ± 319.5, p < .001). Intraclass correlation coefficients for T1 and T2 values were 0.96 (p < 0.001) and 0.83 (p < 0.001), respectively. T2 relaxation time of osteoblastic metastasis and red marrow deposition had no evidence of a difference (osteoblastic metastasis, 57.9 ± 25.0 vs red marrow, 58.0 ± 34.4, p = 0.45), as were the average T2 values of osteolytic metastasis and red marrow deposition (osteolytic metastasis, 45.3 ± 15.1 vs red marrow, 58.0 ± 34.4, p = 0.63).

CONCLUSIONS

We report the feasibility of three-dimensional-magnetic resonance fingerprinting based quantification of bone marrow to differentiate bone metastasis from red marrow. Simultaneous T1 and T2 quantification of metastasis and red marrow deposition was possible in spine and pelvis and showed significant different values with excellent inter-reader agreement.

ADVANCE IN KNOWLEDGE

T1 values from three-dimensional-magnetic resonance fingerprinting might be a useful quantifier for evaluating bone marrow lesions.

Application of dynamic contrast enhanced ultrasound analysis in predicting early response to systemic therapy of intrahepatic cholangiocarcinoma

OBJECTIVE

To evaluate the value of dynamic contrast-enhanced ultrasound (DCE-US) analysis in early prediction of tumor response to systemic treatment in patients with intrahepatic cholangiocarcinoma (ICC).

PATIENTS & METHODS

In this retrospective study, patients diagnosed with ICC by core needle biopsy and histopathological results were included. All patients were diagnosed as advanced stages (stage III/IV) by the 8th edition of the American Joint Committee on Cancer (AJCC)/International Union Against Cancer (UICC) TNM staging system. Liver contrast-enhanced ultrasound (CEUS) examination, DCE-US analysis, CT/MRI, and blood tests were performed in all patients before and 2 months after systemic treatment. CEUS procedure was performed using an ultrasound system (ACUSON Sequoia; Siemens Medical Solutions, Germany) equipped with a 5C1 MHz convex array transducer. Time-intensity curves (TIC) and quantitative parameters were created with VueBox software. According to one-year results of the modified Response Evaluation Criteria in Solid Tumors (m-RECIST) based on CT/MRI, patients were divided into the responder group (RG) and the non-responder group (NRG). Before and 2 months after systemic therapy, the DCE-US perfusion parameters was compared using the paired-sample t test and the Wilcoxon test.

RESULTS

From September 2020 to December 2021, a total of 24 patients diagnosed with advanced ICC were included (11 males, 13 females, mean age 59.4 ± 1.8 years). According to the one year of m-RECIST results, 17 cases (70.8 %) were classified as non-responders by the final m-RECIST criteria, while 7 cases (19.2 %) were responders. Comparing before and 2 months after therapy, the RG took longer time to reach peak intensity, and the peak intensity of TIC was lower. While the TICs of NRG revealed faster enhancement after therapy. Among all DCE-US quantitative parameters, PE (peak enhancement), WiR (wash-in rate), WiPI (wash-in perfusion index) and WoR (wash-out rate) reduced significantly following 2 months of systemic therapy in RG (P < 0.05). Comparing to RG, PE and WiPI decreased slightly 2 months after therapy in NRG (P < 0.05).

CONCLUSIONS

The DCE-US analysis with quantitative parameters has the potential value to make early and quantitative evaluation of treatment response to systemic therapy in ICC patients.

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

Combined visual and quantitative assessment of somatostatin receptor scintigraphy for staging and restaging of neuroendocrine tumors

PURPOSE

Somatostatin receptor scintigraphy (SRS) using 111In-DTPA-DPhe1-octreotide (pentetreotide) has become an integral part of neuroendocrine neoplasm management. The lack of precise quantification is a disadvantage of SRS. This study aimed to adapt the standardized uptake value (SUV) to SRS, establish the SUV range for physiological uptake in the liver, kidney, and spleen, and elucidate the utility of combined visual and quantitative SRS assessment for staging and restaging of neuroendocrine tumors (NETs).

MATERIALS AND METHODS

This study included 21 patients with NETs who underwent 111In-pentetreotide SRS. The SUV of physiological and pathological uptake was calculated using bone single-photon emission computed tomography (SPECT) quantitative analysis software (GI-BONE). For visual analysis, the primary and metastatic lesions were scored visually on planar and SPECT images using a five-point scale. We assessed the relationships between the SUVs of the liver, kidney, and spleen in the dual phase, and among quantitative indices, visual score, and pathological lesions classification.

RESULTS

Sixty-three NEN lesions were evaluated. The mean ± standard deviation maximum SUVs (SUVmax) were liver: 4 h, 2.6 ± 1.0; 24 h, 2.2 ± 1.0; kidney: 4 h, 8.9 ± 1.8; 24 h, 7.0 ± 2.0; and spleen; 4 h, 11.3 ± 4.5; 24 h, 11.5 ± 7.6. Higher SUVmax was significantly associated with higher visual scores on dual-phase SPECT (4 h, p < 0.001; 24 h, p < 0.001) (4 h: scores 3 and 4, p < 0.05; scores 3 and 5: p < 0.01; scores 4 and 5: p < 0.01; 24 h: scores 3 and 4, p = 0.0748; scores 3 and 5: p < 0.01; scores 4 and 5: p < 0.01).

CONCLUSION

We adapted the SUV to SRS and established the range of SUV for physiological uptake in the liver, kidney, and spleen. Combined visual and quantitative assessment is useful for imaging individual lesions in greater detail, and may serve as a new tumor marker of SRS for staging and restaging of NETs.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

A conceptual framework for cautious escalation of anticancer treatment: How to optimize overall benefit and obviate the need for de-escalation trials

BACKGROUND

The developmental workflow of the currently performed phase 1, 2 and 3 cancer trial stages lacks essential information required for the determination of the optimal efficacy threshold of new anticancer regimens. Due to this there is a serious risk of overdosing and/or treating for an unnecessary long time, leading to excess toxicity and a higher financial burden for society. But often post-approval de-escalation trials for dose-optimization and treatment de-intensification are not performed due to failing resources and time. Therefore, the developmental workflow needs to be restructured toward cautious systemic cancer treatment escalation, in order to guarantee optimal efficacy and sustainability.

METHODS

In this manuscript we discuss opportunities to produce the information needed for cautious escalation, based on models of cancer growth and cancer kill kinetics as well as exploratory biomarkers, for the purpose of designing the optimal phase 3 superiority trial. Subsequently, we compare the sample size needed for a phase 3 superiority trial, followed by a necessary de-escalation trial with the sample size needed for a multi-arm phase 3 trial with intervention arms of differing intensity. All essential items are structured within a Framework for Cautious Escalation (FCE). The discussion uses illustrations from the breast cancer setting, but aims to be applicable for all cancers.

RESULTS

The FCE is a promising model of clinical development in oncology to prevent overtreatment and associated issues, especially with regard to the number of repetitive treatment cycles. It will hopefully increase the relevance and success rate of clinical trials, to deliver improved patient-centric outcomes.

Correlation between surrogate endpoints and overall survival in unresectable hepatocellular carcinoma patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

This study aimed to assess the therapeutic effect of immune checkpoint inhibitors (ICIs) in patients with unresectable hepatocellular carcinoma (uHCC) and investigate the correlation between surrogate endpoints and overall survival (OS). A systematic literature search included phase I, II, and III clinical trials comparing ICIs to placebo or other therapies for uHCC treatment. Correlations between OS and surrogate endpoints were evaluated using meta-regression analyses and calculating the surrogate threshold effect (STE). The correlation analysis showed a weak association between OS and progression-free survival (PFS), with an R2 value of 0.352 (95% CI: 0.000-0.967). However, complete response (CR) exhibited a strong correlation with OS (R2 = 0.905, 95% CI: 0.728-1.000). Subgroup analyses revealed high correlations between OS and PFS, CR, stable disease (SD), and DC in phase III trials (R2: 0.827-0.922). For the ICI + IA group, significant correlations were observed between OS and SD, progressive disease (PD), and grade 3-5 immune-related adverse events (irAEs) (R2: 0.713-0.969). Analyses of the correlation between survival benefit and risk of mortality across various time points showed a strong association within the first year (R2: 0.724-0.868) but a weak association beyond one year (R2: 0.406-0.499). In ICI trials for uHCC, PFS has limited utility as a surrogate endpoint for OS, while CR exhibits a strong correlation with OS. Subgroup analyses highlight high correlations between OS and PFS, SD, and DC in phase III trials. Notably, the ICI + IA group shows significant associations between OS and SD, PD, and grade 3-5 irAEs. These findings offer valuable insights for interpreting trial outcomes and selecting appropriate endpoints in future clinical studies involving ICIs for uHCC patients.

Genetic Variants Associated With Response to Platinum-Based Chemotherapy in Non-Small Cell Lung Cancer Patients: A Field Synopsis and Meta-Analysis

Background: Publications on the associations of genetic variants with the response to platinum-based chemotherapy (PBC) in NSCLC patients have surged over the years, but the results have been inconsistent. Here, a comprehensive meta-analysis was conducted to combine eligible studies for a more accurate assessment of the pharmacogenetics of PBC in NSCLC patients. Methods: Relevant publications were searched in PubMed, Scopus, and Web of Science databases through 15 May 2021. Inclusion criteria for eligible publications include studies that reported genotype and allele frequencies of NSCLC patients treated with PBC, delineated by their treatment response (sensitive vs. resistant). Publications on cell lines or animal models, duplicate reports, and non-primary research were excluded. Epidemiological credibility of cumulative evidence was assessed using the Newcastle-Ottawa Scale (NOS) and Venice criteria. Begg's and Egger's tests were used to assess publication bias. Cochran's Q-test and I2 test were used to calculate the odds ratio and heterogeneity value to proceed with the random effects or fixed-effects method. Venice criteria were used to assess the strength of evidence, replication methods and protection against bias in the studies. Results: A total of 121 publications comprising 29,478 subjects were included in this study, and meta-analyses were performed on 184 genetic variants. Twelve genetic variants from 10 candidate genes showed significant associations with PBC response in NSCLC patients with strong or moderate cumulative epidemiological evidence (increased risk: ERCC1 rs3212986, ERCC2 rs1799793, ERCC2 rs1052555, and CYP1A1 rs1048943; decreased risk: GSTM1 rs36631, XRCC1 rs1799782 and rs25487, XRCC3 rs861539, XPC rs77907221, ABCC2 rs717620, ABCG2 rs2231142, and CDA rs1048977). Bioinformatics analysis predicted possible damaging or deleterious effects for XRCC1 rs1799782 and possible low or medium functional impact for CYP1A1 rs1048943. Conclusion: Our results provide an up-to-date summary of the association between genetic variants and response to PBC in NSCLC patients.

Immunotherapy for lung cancer

Immune checkpoint blockers have transformed non-small-cell lung cancer treatment, but they can lead to autoimmune and inflammatory side effects, leading to the concurrent use of immunosuppressive treatments. In this analysis, we delve into the potential of antibodies checkpoint blockade, focusing on CTLA-4 inhibition using ipilimumab, as a groundbreaking cancer immunotherapy. We also concentrate on the role of biomarkers, particularly PD-L1 activity and mutation significance, in predicting the response to programmed cell death protein 1 blockage and the prevalence of side effects associated with immune-related side effects. In describing the patterns of cancer response to immunotherapy, we underline the limitations of response assessment criteria like RECIST and World Health Organization. We also stress the necessity of ongoing studies and clinical trials, standardized guidelines, and additional research to improve response assessment in the era of immunotherapy.

A statistical framework for planning and analysing test-retest studies of repeatability

There is an increasing number of potential quantitative biomarkers that could allow for early assessment of treatment response or disease progression. However, measurements of such biomarkers are subject to random variability. Hence, differences of a biomarker in longitudinal measurements do not necessarily represent real change but might be caused by this random measurement variability. Before utilizing a quantitative biomarker in longitudinal studies, it is therefore essential to assess the measurement repeatability. Measurement repeatability obtained from test-retest studies can be quantified by the repeatability coefficient, which is then used in the subsequent longitudinal study to determine if a measured difference represents real change or is within the range of expected random measurement variability. The quality of the point estimate of the repeatability coefficient, therefore, directly governs the assessment quality of the longitudinal study. Repeatability coefficient estimation accuracy depends on the case number in the test-retest study, but despite its pivotal role, no comprehensive framework for sample size calculation of test-retest studies exists. To address this issue, we have established such a framework, which allows for flexible sample size calculation of test-retest studies, based upon newly introduced criteria concerning assessment quality in the longitudinal study. This also permits retrospective assessment of prior test-retest studies.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients

BACKGROUND

About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection.

AIM

To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.

METHODS

In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.

RESULTS

Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability.

CONCLUSION

The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

Evaluation of Three Imaging Methods to Quantify Key Events in Pelvic Bone Metastasis

BACKGROUND

The purpose of this study is to compare turbo spin echo diffusion-weighted images in radial trajectory (BLADE DWI) with multi-shot echoplanar imaging (RESOLVE DWI) for imaging the metastatic lesion in the pelvic bone to find a correlation between ADC values and standardized uptake values (SUVs) of FDG uptake in PET/CT. The study also seeks to compare the values of metastatic lesions with those of benign bone lesions, specifically red marrow hyperplasia.

METHODS

The retrospective IRB-approved study included patients with bone metastasis and red marrow hyperplasia in the pelvic bone who underwent 3.0 T MRI with BLADE/RESOLVE DWI sequences and F-18 FDG PET/CT within one month. BVC (best value comparator) was used in determining the nature of bone lesions. Apparent diffusion coefficient (ADC) and standardized uptake value (SUV) were measured by a radiologist and a nuclear medicine physician. MRI image quality was graded with a Likert scale regarding the visualization of the sacroiliac joint, sacral neural foramen, hamstring tendon at ischial tuberosity, and tumor border. Signal-to-noise ratio (SNR) and imaging time were compared between the two DWIs. Mean, peak, and maximum SUVs between metastatic and benign red marrow lesions were compared. SUVs and ADC values were compared. AUROC analyses and cut-off values were obtained for each parameter. Mann-Whitney U, Spearman's rho, and Kolmogorov-Smirnov tests were applied using SPSS.

RESULTS

The final study group included 58 bone lesions (19 patients (male: female = 6:13, age 52.5 ± 9.6, forty-four (75.9%) bone metastasis, fourteen (24.1%) benign red marrow hyperplasia). ADCs from BLADE and RESOLVE were significantly higher in bone metastasis than red marrow hyperplasia. BLADE showed higher ADC values, higher anatomical scores, and higher SNR than RESOLVE DWI (p < 0.05). Imaging times were longer for BLADE than RESOLVE (6 min 3 s vs. 3 min 47 s, p < 0.05). There was a poor correlation between ADC values and SUVs (correlation coefficient from 0.04 to 0.31). The AUROC values of BLADE and RESOLVE MRI ranged from 0.892~0.995. Those of PET ranged from 0.877~0.895. The cut-off ADC values between the bone metastasis and red marrow hyperplasia were 355.0, 686.5, 531.0 for BLADE min, max, and average, respectively, and 112.5, 737.0, 273.0 for RESOLVE min, max, and average, respectively. The cut-off SUV values were 1.84, 5.01, and 3.81 for mean, peak, and max values, respectively (p < 0.05).

CONCLUSIONS

Compared with RESOLVE DWI, BLADE DWI showed improved image quality of pelvic bone MRI in the aspect of anatomical depiction and SNR, higher ADC values, albeit longer imaging time. BLADE and RESOLVE could differentiate bone metastasis and red marrow hyperplasia with quantifiable cut-off values. Further study is necessary to evaluate the discrepancy between the quantifiers between PET and MRI.

Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker

PURPOSE

To evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM).

METHODS

Patients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two-three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated.

RESULTS

Of the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652-0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586-0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537-0.86) in the validation set.

CONCLUSIONS

Baseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.

Prognostic value of blood-based protein biomarkers in non-small cell lung cancer: A critical review and 2008-2022 update

BACKGROUND

Therapeutic possibilities for non-small cell lung cancer (NSCLC) have considerably increased during recent decades.

OBJECTIVE

To summarize the prognostic relevance of serum tumor markers (STM) for early and late-stage NSCLC patients treated with classical chemotherapies, novel targeted and immune therapies.

METHODS

A PubMed database search was conducted for prognostic studies on carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase, squamous-cell carcinoma antigen, progastrin-releasing-peptide, CA125, CA 19-9 and CA 15-3 STMs in NSCLC patients published from 2008 until June 2022.

RESULTS

Out of 1069 studies, 141 were identified as meeting the inclusion criteria. A considerable heterogeneity regarding design, patient number, analytical and statistical methods was observed. High pretherapeutic CYFRA 21-1 levels and insufficient decreases indicated unfavorable prognosis in many studies on NSCLC patients treated with chemo-, targeted and immunotherapies or their combinations in early and advanced stages. Similar results were seen for CEA in chemotherapy, however, high pretherapeutic levels were sometimes favorable in targeted therapies. CA125 is a promising prognostic marker in patients treated with immunotherapies. Combinations of STMs further increased the prognostic value over single markers.

CONCLUSION

Protein STMs, especially CYFRA 21-1, have prognostic potential in early and advanced stage NSCLC. For future STM investigations, better adherence to comparable study designs, analytical methods, outcome measures and statistical evaluation standards is recommended.

A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT

BACKGROUND

We aimed to develop a new tumor response assessment method for lung SBRT.

METHODS

In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (μ) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and μ (RA, RCNR, Rμ) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images.

RESULTS

R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (RC) was determined as 1.1. The results based on RC achieved a 94% accuracy, 94% specificity, and 90% sensitivity.

CONCLUSION

The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.

Efficacy Analysis of Neoadjuvant versus Adjuvant Cisplatin-Paclitaxel Regimens for Initial Treatment of FIGO Stages IB3 and IIA2 Cervical Cancer

BACKGROUND Large cancer lesions are often challenging to treat with surgical intervention alone. Neoadjuvant chemotherapy is frequently used for FIGO stage IB3 and IIA2 cervical cancers to optimize the outcomes of radical surgeries. This study aimed to compare the effectiveness of neoadjuvant chemotherapy, followed by adjuvant chemotherapy and radiotherapy, if necessary, with the traditional approach of adjuvant chemotherapy and radiotherapy after radical hysterectomy in treatment-naïve patients with cervical cancer of specified stages. MATERIAL AND METHODS A total of 245 female patients were administered either 70 to 85 mg/m² cisplatin and 165 to 175 mg/m² paclitaxel every 21 days (2 cycles) prior to radical hysterectomy, followed by adjuvant chemotherapy and radiotherapy if needed (neoadjuvant therapy, NT cohort, n=105), or received adjuvant chemotherapy and radiotherapy after radical hysterectomy adjuvant therapy, AT cohort, n=140). RESULTS In the NT cohort, 76% of patients responded to neoadjuvant chemotherapy, while 24% did not. Adverse operative, intraoperative, and postoperative outcomes were significantly more common among the non-responders (P<0.05). After 5 years, 91% of responders and 72% of non-responders survived without recurrence (P=0.0372), and 3% of responders and 28% of non-responders had died (P=0.0005). CONCLUSIONS The resistance to neoadjuvant chemotherapy is a poor prognostic factor. Neoadjuvant chemotherapy followed by radical hysterectomy and adjuvant chemotherapy/radiotherapy appears to be advantageous for cervical cancer patients who respond well to neoadjuvant chemotherapy.

Apatinib added when NSCLC patients get slow progression with EGFR-TKI: A prospective, single-arm study

BACKGROUND

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) acquired resistance was an inevitably events in NSCLC treatment.

AIMS

Intending to overcome the acquired resistance of EGFR-TKI.

MATERIALS & METHODS

A clinical trial was, we enrolled 12 patients who were slowly progressing on first-generation EGFR-TKI, and added apatinib when the patients got slow progression.

RESULTS

Seven patients were included in the efficacy analysis. The median PFS2 of apatinib combined with EGFR-TKI was 8.2 months (95% CI, 7.3 m-NA), and the total PFS reached 20.9 months (95% CI, 17.3 m-NA) when plus PFS1. All the adverse events were manageable. The median PFS was significantly longer for circulating tumor DNA (ctDNA)-cleared patients (8.4 months; 95% CI, 8.2-NA) than for those ctDNA not cleared (7.1 months; 95% CI, 6.9-NA) (p = 0.0082).

DISCUSSION

The addition of apatinib did improve the duration of first-generation EGFR-TKI use, and the duration was better than the first-line use of third-generation EGFR-TKI.

CONCLUSION

The addition of apatinib when the patients got slow progression after initial EGFR-TKI therapy may be a good treatment option and the side effects are controllable. It is possible to monitor treatment efficacy using ctDNA.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI

OBJECTIVES

While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

METHODS

In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation.

RESULTS

Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001.

CONCLUSIONS

Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

KEY POINTS

• A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

Pretreatment diffusion-weighted imaging for prediction of relapsed and refractory primary central nervous system lymphoma

Objectives

A subset of primary central nervous system lymphoma (PCNSL) has been shown to undergo an early relapsed/refractory (R/R) period after first-line chemotherapy. This study investigated the pretreatment clinical and MRI features to predict R/R in PCNSL, emphasizing the apparent diffusion coefficient (ADC) values in diffusion-weighted imaging (DWI).

Methods

This retrospective study investigated the pretreatment MRI features for predicting R/R in PCNSL. Only patients who had undergone complete preoperative and postoperative MRI follow-up studies were included. From January 2006 to December 2021, 52 patients from two medical institutions with a diagnosis of PCNSL were included (median follow-up time, 26.3 months). Among these, 24 (46.2%) had developed R/R (median time to relapse, 13 months). Cox proportional hazard regression analyses were performed to determine hazard ratios for all parameters.

Results

Significant predictors of R/R in PCNSL were female sex, complete response (CR) to first-line chemotherapy, and ADC value/ratio (p < 0.05). Cut-off points of ADC values and ADC ratios for prediction of R/R were 0.68 × 10-3 mm2/s and 0.97, with AUCs of 0.78 and 0.77, respectively (p < 0.05). Multivariate Cox proportional hazards analysis showed that failure of CR to first-line chemotherapy and low ADC values (<0.68 × 10-3 mm2/s) were significant risk factors for R/R, with hazard ratios of 5.22 and 14.45, respectively (p < 0.05). Kaplan-Meier analysis showed that lower ADC values and ratios predicted significantly shorter progression-free survival (p < 0.05).

Conclusion

Pretreatment ADC values in DWI offer quantitative valuable information for the treatment planning in PCNSL.

Recent Advances in Image-Guided Locoregional Therapies for Primary Liver Tumors

Primary liver cancer is the leading cause of cancer-related deaths worldwide. with incidences predicted to rise over the next several decades. Locoregional therapies, such as radiofrequency or microwave ablation, are described as image-guided percutaneous procedures, which offer either a curative intent for early-stage hepatocellular carcinoma or bridging/downstaging for surgical resection or transplantation. Catheter-driven locoregional therapies, such as transarterial chemoembolization and radioembolization, induce tumor hypoxia, can be palliative, and improve survival for early-to-intermediate hepatocellular carcinoma and unresectable intrahepatic cholangiocarcinoma. Herein, we provide a comprehensive overview of the antineoplastic mechanisms underpinning locoregional therapies, different treatment approaches, and the current state of the literature for the efficacy of locoregional therapies for primary liver cancer. We also discuss emerging advancements, such as the adjuvant use of immunotherapies and molecular targeting agents with locoregional therapy, for the treatment of primary liver cancer.

Immune checkpoint inhibition: a future guided by radiology

The limitation of the function of antitumour immune cells is a common hallmark of cancers that enables their survival. As such, the potential of immune checkpoint inhibition (ICI) acts as a paradigm shift in the treatment of a range of cancers but has not yet been fully capitalised. Combining minimally and non-invasive locoregional therapies offered by radiologists with ICI is now an active field of research with the aim of furthering therapeutic capabilities in medical oncology. In parallel to this impending advancement, the "imaging toolbox" available to radiologists is also growing, enabling more refined tumour characterisation as well as greater accuracy in evaluating responses to therapy. Options range from metabolite labelling to cellular localisation to immune checkpoint screening. It is foreseeable that these novel imaging techniques will be integrated into personalised treatment algorithms. This growth in the field must include updating the current standardised imaging criteria to ensure they are fit for purpose. Such criteria is crucial to both appropriately guide clinical decision-making regarding next steps of treatment, but also provide reliable prognosis. Quantitative approaches to these novel imaging techniques are also already being investigated to further optimise personalised therapeutic decision-making. The therapeutic potential of specific ICIs and locoregional therapies could be determined before administration thus limiting unnecessary side-effects whilst maintaining efficacy. Several radiological aspects of oncological care are advancing simultaneously. Therefore, it is essential that each development is assessed for clinical use and optimised to ensure the best treatment decisions are being offered to the patient. In this review, we discuss state of the art advances in novel functional imaging techniques in the field of immuno-oncology both pre-clinically and clinically.