Response Evaluation Criteria in Solid Tumors (RECIST) following neoadjuvant chemotherapy in osteosarcoma

Guenther, Lillian M.; Rowe, R. Grant; Acharya, Patricia T.; Swenson, David W.; Meyer, Stephanie C.; Clinton, Catherine; Guo, Deliang; Sridharan, Makuteswaran; London, Wendy B.; Grier, Holcombe E.; Ecklund, Kirsten; Janeway, Katherine A.

doi:10.1002/pbc.26896

Cited by 47 publications

(40 citation statements)

References 27 publications

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“…Traditional temporal monitoring of tumors often has been limited to predefined metrics including tumor longest diameter measured through the established Response Evaluation Criteria in Solid Tumors (RECIST) and World Health Organization (WHO) criteria for estimating tumor burden and determining treatment response. In addition to being criticized as oversimplifying the complex tumor geometry captured through sophisticated imaging instruments, the generalizability and efficacy of such criteria have been questioned, as in the case of osseous lesions, for which chemotherapy—which has proven to improve survival—does not result in radiographic responses as measured by RECIST . AI‐based monitoring, however, is able to capture a large number of discriminative features across images over time that go beyond those measured by human readers.…”

Section: Ai Applications In Cancer Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,221

820

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Section: Ai Applications In Cancer Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,221

820

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“…The exclusion criteria of samples are as follows: i) Patients who were pregnant when they were afflicted with ovarian cancer, ii) patients who were simultaneously afflicted with ovarian cancer and other malignant tumor types and iii) patients whose case or data were incomplete. According to the radiologic Response Evaluation Criteria in Solid Tumors (RECIST) guidelines, the 58 clinical tissues were divided into two groups immediately following administration of the chemotherapy regime (15). According to their response to chemotherapy, they were divided into the 'sensitive' (complete or partial response) and 'insensitive' (stable or progressive disease) groups.…”

Section: Patientsmentioning

confidence: 99%

MicroRNA-149 suppresses the proliferation and increases the sensitivity of ovarian cancer cells to cisplatin by targeting X-linked inhibitor of apoptosis

et al. 2018

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Abstract. Currently, ovarian cancer is identified as one of the leading causes of cancer-associated mortality in females. Despite numerous efforts that were made on developing novel treatments for ovarian cancer, the survival rate remains unsatisfactory. Considering the important regulatory role of miRNAs in different types of cancer, the present study aims to identify a novel therapeutic target for treatment of ovarian cancer. The expression of miR-149 was detected using reverse transcription-quantitative polymerase chain reaction in cancerous and normal cells. Furthermore, the effects of miR-149 on ovarian cancer cell activities were investigated using MTT assay, colony formation, flow cytometry and western blotting analysis. In the present study, it was revealed that microRNA (miR)-149 was significantly downregulated in ovarian cancer tissues and cell lines, and that the miR-149 expression was correlated with the patient prognosis. In addition, it was observed that forced expression of miR-149 increased the sensitivity of ovarian cancer cell to cisplatin. Based on bioinformatics analysis and luciferase assay, X-linked inhibitor of apoptosis (XIAP) was identified as a direct target gene of miR-149 in ovarian cancer cells. It was also demonstrated that XIAP expression was upregulated in the ovarian cancer tissues and cell lines, while it was negatively correlated with miR-149 in these tissues and cells. Furthermore, results revealed that ectopic expression of XIAP was able to abolish the miR-149-enhanced cell sensitivity to cisplatin. In conclusion, the present study revealed that miR-149 functioned as a tumor suppressor in the progression of ovarian cancer, increasing the sensitivity of ovarian cancer cells to cisplatin treatment.

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“…This creates a need for smaller clinical trials that can vet more potential therapies more quickly. 87 Thus there are clear efficacy bars for phase II trials. Fortunately, a dedicated analysis of past trials has been completed and accepted as a historical control that trialists can use to power and evaluate future clinical trials in patients with relapse OS.…”

Section: Introductionmentioning

confidence: 99%

Provocative questions in osteosarcoma basic and translational biology: A report from the Children's Oncology Group

Roberts

Lizardo

Reed

et al. 2019

Cancer

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Patients who are diagnosed with osteosarcoma (OS) today receive the same therapy that patients have received over the last 4 decades. Extensive efforts to identify more effective or less toxic regimens have proved disappointing. As we enter a postgenomic era in which we now recognize OS not as a cancer of mutations but as one defined by p53 loss, chromosomal complexity, copy number alteration, and profound heterogeneity, emerging threads of discovery leave many hopeful that an improving understanding of biology will drive discoveries that improve clinical care. Under the organization of the Bone Tumor Biology Committee of the Children's Oncology Group, a team of clinicians and scientists sought to define the state of the science and to identify questions that, if answered, have the greatest potential to drive fundamental clinical advances. Having discussed these questions in a series of meetings, each led by invited experts, we distilled these conversations into a series of seven Provocative Questions. These include questions about the molecular events that trigger oncogenesis, the genomic and epigenomic drivers of disease, the biology of lung metastasis, research models that best predict clinical outcomes, and processes for translating findings into clinical trials. Here, we briefly present each Provocative Question, review the current scientific evidence, note the immediate opportunities, and speculate on the impact that answered questions might have on the field. We do so with an intent to provide a framework around which investigators can build programs and collaborations to tackle the hardest problems and to establish research priorities for those developing policies and providing funding. Cancer 2019;125:3514-3525.

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Response Evaluation Criteria in Solid Tumors (RECIST) following neoadjuvant chemotherapy in osteosarcoma

Abstract: PD by RECIST predicts poor outcome in localized disease patients. In bone lesions, chemotherapy proven to improve overall survival does not result in radiographic responses as measured by RECIST. Further investigation of RECIST in pulmonary metastatic disease in osteosarcoma is needed.

Cited by 47 publications

References 27 publications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Artificial intelligence in cancer imaging: Clinical challenges and applications

MicroRNA-149 suppresses the proliferation and increases the sensitivity of ovarian cancer cells to cisplatin by targeting X-linked inhibitor of apoptosis

Provocative questions in osteosarcoma basic and translational biology: A report from the Children's Oncology Group

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