Assessing the response to oncological treatments is paramount for determining the prognosis and defining the best treatment for each patient. Several biomarkers, including imaging, can be used, but standardization is fundamental for consistency and reliability. Tumor response evaluation criteria have been defined by international groups for application in pharmaceutical clinical trials evaluating new drugs or therapeutic strategies. RECIST 1.1 criteria are exclusively based on unidimensional lesion measurements; changes in tumor size are used as surrogate imaging biomarkers to correlate with patient outcomes. However, increased tumor size does not always reflect tumor progression. The introduction of immunotherapy has led to the development of new criteria (iRECIST, Level of Evidence (LoE) Ib) that consider the possibility that an increase in disease burden is secondary to the immune response instead of progression, with the new concept of Unconfirmed Progressive Disease (a first progression event which must be confirmed on follow-up). Specific criteria were devised for HCC (mRECIST, LoE IV), which measure only enhancing HCC portions to account for changes after local therapy. For GIST treated with imatinib, criteria were developed to account for the possible increase in size reflecting a response rather than a progression by assessing both tumor size and density on CT (Choi, LoE II). This article provides concise and relevant practice recommendations aimed at general radiologists to help choose and apply the most appropriate criteria for assessing response to treatment in different oncologic scenarios. Though these criteria were developed for clinical trials, they may be applied in clinical practice as a guide for day-to-day interpretation. KEY POINTS: Response evaluation criteria, designed for use in clinical trials, might serve as a surrogate biomarker for overall survival. RECIST 1.1 defines measurable and non-measurable disease among which target lesions and non-target lesions are selected at baseline as reference for follow-ups. Some therapies and/or cancers require the use of different criteria, such as iRECIST, mRECIST, and Choi criteria.

We investigated the association of CA125 response with prognosis and RECIST response/progressive disease (PD) criteria in recurrent high grade serous ovarian cancer (HGSOC) patients treated with a cell cycle checkpoint kinase 1 inhibitor (CHK1i), prexasertib. 81 patients had measurable disease per RECISTv1.1, of which 72 and 70 were measurable by Gynecologic Cancer InterGroup (GCIG) CA125 response and PD criteria, respectively. Univariate and multivariate analyses showed that GCIG CA125 response (n = 32) is associated with improved progression-free survival (PFS) and overall survival (OS) compared to no GCIG CA125 response (n = 40) (median PFS 8.0 vs. 3.5 months [HR: 0.30, 95% CI: 0.18-0.51, p < 0.0001]; median OS 19.8 vs. 10.0 months [HR: 0.38, 95% CI: 0.23-0.64, p < 0.001]) independent of BRCA mutation status, platinum-sensitivity, previous PARP inhibitor therapy, ECOG performance status, and FIGO stage. Notably, GCIG CA125 response had a high negative predictive value (NPV: 93%, 95% CI: 80-98), but poor positive predictive value (PPV: 53%, 95% CI: 35-71) in predicting RECIST response. CA125 PD criteria also showed poor concordance with RECIST PD (PPV 56%, 95% CI: 40-71; NPV 33%, 95% CI: 17-54). Therefore, serum CA125 may be useful as a highly accessible prognostic and predictive biomarker to CHK1i therapy in recurrent HGSOC.

OBJECTIVE: To evaluate the performance of hepatobiliary MRI parameters as predictors of clinical response to chemotherapy in patients with initially unresectable colorectal cancer liver metastases (CRLM).
METHODS: Eighty-five patients with initially unresectable CRLM were retrospectively enrolled from two hospitals and scanned using gadobenate dimeglumine-enhanced MRI before treatment. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Conventional parameters (i.e., signal intensity [SI]) and radiomics features of portal venous phase (PVP) and hepatobiliary phase (HBP) images were analyzed between the responders and non-responders. Next, the combined model was constructed, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated. The relationship between the combined model and progression-free survival (PFS) was analyzed using Cox regression.
RESULTS: Of the 85 patients from two hospitals, 42 were in the response group, and 43 were in the non-response group. Upon conducting five-fold cross-validation, the normalized relative enhancement (NRE) of CRLM during the PVP yielded an AUC of 0.625. Additionally, a radiomics feature derived from the tumor area in the HBP achieved an AUC of 0.698, while a separate feature extracted from the peritumoral region in the HBP recorded an AUC of 0.709. The model that integrated these three features outperformed the individual features, achieving an AUC of 0.818. Furthermore, the combined model exhibited a significant correlation with PFS (P < 0.001).
CONCLUSIONS: The combined model, based on baseline hepatobiliary MRI, aids in predicting chemotherapeutic response and PFS in patients with initially unresectable CRLM.

OBJECTIVE: In this study, we aimed to evaluate the potential of routine blood markers, serum tumour markers and their combination in predicting RECIST-defined progression in patients with stage IV non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors.
METHODS: We employed time-varying statistical models and machine learning classifiers in a Monte Carlo cross-validation approach to investigate the association between RECIST-defined progression and blood markers, serum tumour markers and their combination, in a retrospective cohort of 164 patients with NSCLC.
RESULTS: The performance of the routine blood markers in the prediction of progression free survival was moderate. Serum tumour markers and their combination with routine blood markers generally improved performance compared to routine blood markers alone. Elevated levels of C-reactive protein (CRP) and alkaline phosphatase (ALP) ranked as the top predictive routine blood markers, and CYFRA 21.1 was consistently among the most predictive serum tumour markers. Using these classifiers to predict overall survival yielded moderate to high performance, even when cases of death-defined progression were excluded. Performance varied across the treatment journey.
CONCLUSIONS: Routine blood tests, especially when combined with serum tumour markers, show moderate predictive value  of RECIST-defined progression in NSCLC patients receiving immune checkpoint inhibitors. The relationship between overall survival and RECIST-defined progression may be influenced by confounding factors.

OBJECTIVE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results.
METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning-/deep learning-based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability.
RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement.
CONCLUSIONS: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

OBJECTIVE: To assess the eligibility of patients with advanced or recurrent solid malignancies presented to a molecular tumor board (MTB) at a large precision oncology center for inclusion in trials with the endpoints objective response rate (ORR) or duration of response (DOR) based on Response Evaluation Criteria in Solid Tumors (RECIST version 1.1).
METHODS: Prospective patients with available imaging at the time of presentation in the MTB were included. Imaging data was reviewed for objectifiable measurable disease (MD) according to RECIST v1.1. Additionally, we evaluated the patients with MD for representativeness of the identified measurable lesion(s) in relation to the overall tumor burden.
RESULTS: 262 patients with different solid malignancies were included. 177 patients (68%) had MD and 85 (32%) had non-measurable disease (NMD) at the time point of MTB presentation in accordance with RECIST v1.1. MD was not representative of the overall tumor burden in eleven patients (6%). The main reasons for NMD were lesions with longest diameter shorter than 10 mm (22%) and non-measurable peritoneal carcinomatosis (18%). Colorectal cancer and malignant melanoma displayed the highest rates of MD (> 75%). In contrast, gastric cancer, head and neck malignancies, and ovarian carcinoma had the lowest rates of MD (< 55%). In case of MD, the measurable lesions were representative of the overall tumor burden in the vast majority of cases (94%).
CONCLUSIONS: Approximately one third of cancer patients with advanced solid malignancies are not eligible for treatment response assessment in trials with endpoints ORR or DOR at the time of MTB presentation. The rate of patients eligible for trials with imaging endpoints differs significantly based on the underlying malignancy and should be taken under consideration during the planning of new precision oncology trials.

Assessing treatment efficacy for brain tumours has evolved since its inception with the introduction of MacDonald\'s criteria, which pioneered the utility of imaging to determine an objective and quantifiable response to treatment. This criterion failed to distinguish pseudo response or progression from progression and did not account for non-enhancing disease therefore; the response assessment in neuro-oncology (RANO) working group was established to account for these limitations. Since, its commencement it has worked to determine response assessment for multiple tumours. As paediatric tumours exhibit heterogeneous and variable-enhancing characteristics, the response assessment in paediatric neuro-oncology (RAPNO) working group was formed to create separate criteria. Six response criteria have been published to date, and the article summarizes them.

Tyrosine kinase inhibitors (TKIs) are standard first-line treatments for advanced non-small-cell lung cancer (NSCLC) with driver gene mutations. The Response Evaluation Criteria in Solid Tumors (RECIST) are limited in predicting long-term patient benefits. A tumour marker-based evaluation criteria, RecistTM, was used to investigate the potential for assessing targeted-therapy efficacy in lung cancer treatment.
We retrospectively analysed patients with stage IIIA-IV NSCLC and driver gene mutations, whose baseline tumour marker levels exceeded the pre-treatment cut-off value three-fold and who received TKI-targeted therapy as a first-line treatment. We compared efficacy, progression-free survival (PFS), and overall survival (OS) between RecistTM and RECIST.
The median PFS and OS differed significantly among treatment-response subgroups based on RecistTM but not RECIST. The predicted 1-, 2-, and 3-year disease-progression risk, according to area under the receiver operating characteristic curve, as well as the 1-, 3-, and 5-year mortality risk, differed significantly between RecistTM and RECIST. The median PFS and OS of tmCR according to RecistTM, was significantly longer than (CR+PR) according to RECIST. Imaging analysis revealed that the ΔPFS was 11.27 and 6.17 months in the intervention and non-intervention groups, respectively, suggesting that earlier intervention could extend patients\' PFS.
RecistTM can assess targeted-therapy efficacy in patients with advanced NSCLC and driver gene mutations, along with tumour marker abnormalities. RecistTM surpasses RECIST in predicting short- and long-term patient benefits, and allows the early identification of patients resistant to targeted drugs, enabling prompt intervention and extending the imaging-demonstrated time to progression.

UNASSIGNED: Response Evaluation Criteria in Solid Tumors (RECIST) are commonly used to assess therapeutic response in clinical trials but not in routine care; thus, RECIST-based end points are difficult to include in observational studies. Clinician-anchored approaches for measuring clinical response have been validated but not widely compared with clinical trial data, limiting their use as evidence for clinical decision-making.
UNASSIGNED: To compare response- and progression-based end points in clinical trial and observational cohorts of patients with non-small cell lung cancer (NSCLC).
UNASSIGNED: This retrospective cohort study used patient-level data from the IMpower132 trial (conducted April 7, 2016, to May 31, 2017) and a nationwide electronic health record (EHR)-derived deidentified database (data collected January 1, 2011, to March 31, 2022). Patients in the observational cohort were selected according to the inclusion and exclusion criteria of the IMpower132 trial. All patients in the observational cohort had stage IV NSCLC.
UNASSIGNED: All patients were randomized to or received first-line carboplatin or cisplatin plus pemetrexed.
UNASSIGNED: End points included response rates, duration of response, and progression-free survival, compared between the trial and observational cohorts before and after weighting. Response rates for the observational cohort were derived from the EHR.
UNASSIGNED: A total of 769 patients met inclusion criteria, 494 in the observational cohort (median [IQR] age, 67 [60-74] years; 228 [46.2%] female; 45 [9.1%] Black or African American; 352 [71.3%] White; 53 [10.7%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial) and 275 in the trial cohort (median [IQR] age, 63 [56-68] years; 90 [32.7%] female; 4 [1.5%] Black or African American; 194 [70.5%] White; 65 [23.6%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial). All 3 end points were comparable between the study cohorts. Trial patients had a higher number of response assessments compared with patients in the weighted observational cohort. The EHR-derived response rate was numerically higher than the objective response rate after weighting (100.3 of 249.3 [40.2%] vs 105 of 275 [38.2%]) due to higher rates of observed partial response than RECIST-based partial response. Among patients with at least 1 response assessment, the EHR-derived response rate remained higher than the objective response rate (100.3 of 193.4 [51.9%] vs 105 of 256 [41.0%]) due to a higher proportion of patients in the observational cohort with no response assessment.
UNASSIGNED: In this study, response- and progression-based end points were similar between clinical trial and weighted observational cohorts, which increases confidence in the reliability of observational end points and can inform their interpretation in relation to trial end points. Additionally, the difference observed in response rates (including vs excluding patients with no response assessment) highlights the importance of future research adopting this 2-way approach when evaluating the relationship of EHR-derived and objective response rates.

As the management of gastrointestinal malignancy has evolved, tumor response assessment has expanded from size-based assessments to those that include tumor enhancement, in addition to functional data such as those derived from PET and diffusion-weighted imaging. Accurate interpretation of tumor response therefore requires knowledge of imaging modalities used in gastrointestinal malignancy, anticancer therapies, and tumor biology. Targeted therapies such as immunotherapy pose additional considerations due to unique imaging response patterns and drug toxicity; as a consequence, immunotherapy response criteria have been developed. Some gastrointestinal malignancies require assessment with tumor-specific criteria when assessing response, often to guide clinical management (such as watchful waiting in rectal cancer or suitability for surgery in pancreatic cancer). Moreover, anatomic measurements can underestimate therapeutic response when applied to molecular-targeted therapies or locoregional therapies in hypervascular malignancies such as hepatocellular carcinoma. In these cases, responding tumors may exhibit morphologic changes including cystic degeneration, necrosis, and hemorrhage, often without significant reduction in size. Awareness of pitfalls when interpreting gastrointestinal tumor response is required to correctly interpret response assessment imaging and guide appropriate oncologic management. Data-driven image analyses such as radiomics have been investigated in a variety of gastrointestinal tumors, such as identifying those more likely to respond to therapy or recur, with the aim of delivering precision medicine. Multimedia-enhanced radiology reports can facilitate communication of gastrointestinal tumor response by automatically embedding response categories, key data, and representative images. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.